Three dimensional estimation of vegetation moisture

Three dimensional estimation of vegetation moisture content

using dual-wavelength terrestrial laser scanning

Ahmed Elsherif

Thesis submitted for the degree of Doctor of Philosophy

School of Engineering

Newcastle University

February 2020

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Abstract

Leaf Equivalent Water Thickness (EWT) is a water status metric widely used in vegetation

health monitoring. Optical Remote Sensing (RS) data, spaceborne and airborne, can be used to

estimate canopy EWT at landscape level, but cannot provide information about EWT vertical

heterogeneity, or estimate EWT predawn. Dual-wavelength Terrestrial Laser Scanning (TLS)

can overcome these limitations, as TLS intensity data, following radiometric corrections, can

be used to estimate EWT in three dimensions (3D). In this study, a Normalized Difference

Index (NDI) of 808 nm wavelength, utilized in the Leica P20 TLS instrument, and 1550 nm

wavelength, employed in the Leica P40 and P50 TLS systems, was used to produce 3D EWT

estimates at canopy level. Intensity correction models were developed, and NDI was found to

be able to minimize the incidence angle and leaf internal structure effects.

Multiple data collection campaigns were carried out. An indoors dry-down experiment revealed

a strong correlation between NDI and EWT at leaf level. At canopy level, 3D EWT estimates

were generated with a relative error of 3 %. The method was transferred to a mixed-species

broadleaf forest plot and 3D EWT estimates were generated with relative errors < 7 % across

four different species. Next, EWT was estimated in six short-rotation willow plots during leaf

senescence with relative errors < 8 %. Furthermore, a broadleaf mixed-species urban tree plot

was scanned during and two months after a heatwave, and EWT temporal changes were

successfully detected. Relative error in EWT estimates was 6 % across four tree species. The

last step in this research was to study the effects of EWT vertical heterogeneity on forest plot

reflectance. Two virtual forest plots were reconstructed in the Discrete Anisotropic Radiative

Transfer (DART) model. 3D EWT estimates from TLS were utilized in the model and Sentinel-

2A bands were simulated. The simulations revealed that the top four to five metres of canopy

dominated the plot reflectance. The satellite sensor was not able to detect severe water stress

that started in the lower canopy layers.

This study showed the potential of using dual-wavelength TLS to provide important insights

into the EWT distribution within the canopy, by mapping the EWT at canopy level in 3D. EWT

was found to vary vertically within the canopy, with EWT and Leaf Mass per Area (LMA)

being highly correlated, suggesting that sun leaves were able to hold more moisture than shade

leaves. The EWT vertical profiles varied between species, and trees reacted in different ways

during drought conditions, losing moisture from different canopy layers. The proposed method

can provide time series of the change in EWT at very high spatial and temporal resolutions, as

TLS instruments are active sensors, independent of the solar illumination. It also has the

potential to provide EWT estimates at the landscape level, if coupled with automatic tree

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segmentation and leaf-wood separation techniques, and thus filling the gaps in the time series

produced from satellite data. In addition, the technique can potentially allow the

characterisation of whole-tree leaf water status and total water content, by combining the EWT

estimates with Leaf Area Index (LAI) measurements, providing new insights into forest health

and tree physiology.

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Acknowledgements

I would like to express my sincere gratitude to my supervisors Dr Rachel Gaulton and

Prof. Jon Mills for their support, encouragement, advice and assistance throughout the years of

my PhD. Thank you for having faith in me. This thesis would not have been possible without

your help and support.

My appreciation is due to the Egyptian Ministry of Higher Education for funding my PhD

research, and for the Egyptian Cultural and Educational Bureau in London for supporting me

during the years of my PhD.

I would like to thank Leica Geosystems UK for loan of the P40 and P50 instruments used in

this research. I also would like to thank the Natural Environment Research Council (NERC) for

providing the Spectralon panels used in the calibration experiments. I am also very grateful to

Prof. Mark Danson for hosting the dry-down experiment at Salford University, Manchester,

UK, and for my friend Fadal Sasse for his help during the experiment. Many thanks to Prof.

Yadvinder Malhi, Dr Alexander Shenkin, Mr Nigel Fisher, and of course my friend and

colleague Zheng Wang for their help and support at Wytham Woods. In addition, Prof.

Yadvinder Malhi and Dr Alexander Shenkin have contributed to the results interpretation

presented in Sections 5.5.1 and 5.5.4. Many thanks to Mr Martin Robertson for actually

teaching me how to use a TLS instrument.

I would like to thank the entire Geospatial Engineering group at Newcastle University for

making me feel at home here in Newcastle. Special thanks to my best friends Elias Berra, Maria

Peppa, Magdalena Śmigaj, Kzr Moreri, David Walker, Marcos Baluja, Nurten Akgun, Katarina

Vardic, and Marine Roger for all the good time we had, and for the special moments that have

become memories that will live forever.

Lastly, I would like to thank my entire family, especially my mother, and my friends and

colleagues in Egypt for their unconditional support throughout my study. I also would like to

thank Prof. Hafez Afify for helping me become the researcher I am today.

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Related publications

The following publications contain work related to or derived from this thesis:

Elsherif, A., Gaulton, R. and Mills, J. (2019) ‘Four dimensional mapping of vegetation moisture

content using dual-wavelength terrestrial laser scanning’, Remote Sensing, 11, 2311.

Elsherif, A., Gaulton, R., Shenkin, A., Malhi, Y. and Mills, J. (2019) 'Three dimensional

mapping of forest canopy equivalent water thickness using dual-wavelength terrestrial laser

scanning', Agricultural and Forest Meteorology, 276, 107627.

Elsherif, A., Gaulton, R. and Mills, J. (2018) 'Estimation of vegetation water content at leaf and

canopy level using dual-wavelength commercial terrestrial laser scanners', Interface Focus,

8(2), 59-69.

Elsherif, A., Gaulton, R., and Mills, J. P. (2019) ‘The potential of dual-wavelength terrestrial

laser scanning in 3D canopy fuel moisture content mapping’, International Archives of the

Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W13, 975-979.

Elsherif, A., Gaulton, R., and Mills, J. P. (2019) ‘Measuring leaf equivalent water thickness of

short-rotation coppice willow canopy using terrestrial laser scanning’, IEEE International

Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan, 28 July – 2 August.

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Table of Contents

List of Figures .......................................................................................................................... xi

List of Tables ......................................................................................................................... xvii

List of Abbreviations ............................................................................................................. xix

Chapter 1. Introduction ........................................................................................................... 1

1.1 Research context .............................................................................................................. 1

1.2 Research aim .................................................................................................................... 6

1.2.1 Research questions ................................................................................................... 6

1.2.2 Research objectives .................................................................................................. 6

1.3 Thesis structure ................................................................................................................ 7

Chapter 2. Remote sensing of leaf equivalent water thickness ............................................ 9

2.1 Introduction ...................................................................................................................... 9

2.2 Measuring EWT ............................................................................................................... 9

2.3 Estimating EWT from optical RS data .......................................................................... 10

2.3.1 Vegetation indices .................................................................................................. 12

2.3.2 Radiative transfer models ....................................................................................... 16

2.3.3 Vertical heterogeneity in canopy biochemical traits .............................................. 21

2.4 Terrestrial laser scanning ............................................................................................... 22

2.4.1 TLS intensity data .................................................................................................. 24

2.4.2 Multispectral and hyperspectral LiDAR ................................................................ 27

2.4.3 TLS and vegetation biochemical traits ................................................................... 29

2.5 Summary ........................................................................................................................ 32

Chapter 3. Combining TLS intensity data from different instruments in the NDI ......... 35

3.1 Introduction .................................................................................................................... 35

3.2 TLS instruments ............................................................................................................. 35

3.3 TLS data processing to generate 3D EWT point clouds ................................................ 37

3.3.1 Scan setup .............................................................................................................. 37

3.3.2 Point cloud registration .......................................................................................... 38

3.3.3 Point matching and NDI calculation ...................................................................... 39

3.3.4 Building the NDI – EWT estimation model .......................................................... 40

3.3.5 Generating the EWT point cloud ........................................................................... 41

3.4 TLS intensity data calibration ........................................................................................ 42

3.4.1 Concept of range effect intensity calibration models ............................................. 42

3.4.2 Determining the intensity-range relationships ....................................................... 43

3.4.3 Determining the intensity-reflectance relationships............................................... 43

3.4.4 Validating the intensity calibration models............................................................ 43

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3.5 The ability of NDI to minimize the incidence angle effects ......................................... 45

3.6 The ability of NDI to minimize the leaf structure effects ............................................. 46

3.6.1 PROSPECT model ................................................................................................ 47

3.6.2 The effects of leaf structure coefficient on the NDI .............................................. 47

3.6.3 The effects of LMA on the NDI ............................................................................ 48

3.6.4 The effects of leaf structure coefficient on the NDI-EWT relationship ................ 48

3.6.5 The effects of LMA on the NDI-EWT relationship .............................................. 48

3.7 Results and discussion ................................................................................................... 48

3.7.1 TLS intensity data calibration................................................................................ 48

3.7.2 Validating the intensity calibration models ........................................................... 53

3.7.3 The ability of NDI to minimize the incidence angle effect ................................... 55

3.7.4 The ability of NDI to minimize the leaf structure effects ...................................... 56

3.8 Summary ....................................................................................................................... 59

Chapter 4. EWT estimation in indoors dry-down experiment .......................................... 61

4.1 Introduction ................................................................................................................... 61

4.2 Experimental setup ........................................................................................................ 61

4.3 Leaf sampling and biochemistry measurements ........................................................... 62

4.3.1 Deciduous leaves ................................................................................................... 62

4.3.2 Conifer needles leaves ........................................................................................... 63

4.4 Canopy level data processing and analysis ................................................................... 63

4.4.1 Point cloud processing ........................................................................................... 63

4.4.2 Removing the woody materials ............................................................................. 63

4.4.3 Studying the effect of wood on the EWT estimations ........................................... 64

4.4.4 Detecting the change in EWT ................................................................................ 64

4.4.5 Studying the EWT vertical profiles ....................................................................... 64


4.5.1 Leaf level results .................................................................................................... 64

4.5.2 Canopy level EWT estimation ............................................................................... 65

4.5.3 Studying the effect of wood on the EWT estimations ........................................... 68

4.5.4 Detecting the change in EWT ................................................................................ 69

4.5.5 Studying the EWT vertical profiles ....................................................................... 70

4.6 Summary ....................................................................................................................... 71

Chapter 5. Mapping of forest canopy EWT in three dimensions ...................................... 73

5.1 Introduction ................................................................................................................... 73

5.2 Study area and TLS scanning setup .............................................................................. 74

5.3 Leaf sampling and biochemistry measurements ........................................................... 76

5.3.1 Samples for building the EWT estimation model ................................................. 76

5.3.2 Samples for validation of the EWT estimation ..................................................... 76

5.4 TLS point cloud processing ........................................................................................... 77

5.4.1 Point cloud registration and filtering ..................................................................... 77

5.4.2 Generating the EWT point clouds ......................................................................... 78

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5.4.3 Validating the EWT estimations ............................................................................ 78


5.5.1 Leaf level results .................................................................................................... 79

5.5.2 EWT point clouds .................................................................................................. 82

5.5.3 Validating the EWT estimations ............................................................................ 84

5.5.4 EWT vertical profiles ............................................................................................. 86

5.6 Summary ........................................................................................................................ 88

Chapter 6. Transferability of the EWT estimation approach to different sites ............... 89

6.1 Introduction .................................................................................................................... 89

6.2 Willow dataset ............................................................................................................... 89

6.2.1 Study area ............................................................................................................... 89

6.2.2 Experiment setup .................................................................................................... 91

6.2.3 Leaf sampling and biochemistry measurements .................................................... 92

6.2.4 Point cloud processing ........................................................................................... 93

6.3 Willow dataset results and discussion ........................................................................... 93

6.3.1 Leaf level ................................................................................................................ 93

6.3.2 Canopy level........................................................................................................... 95

6.4 Exhibition Park dataset .................................................................................................. 98

6.4.1 Study area ............................................................................................................... 98

6.4.2 Leaf sampling and biochemistry measurements .................................................... 99

6.4.3 Point cloud processing ......................................................................................... 101

6.5 Exhibition Park dataset results and discussion ............................................................ 102

6.5.1 Leaf level .............................................................................................................. 102

6.5.2 Canopy level......................................................................................................... 107

6.5.3 Detecting the temporal changes in EWT ............................................................. 110

6.5.4 Vertical profiles of EWT ...................................................................................... 111

6.6 Species- and site-independent EWT estimation model ............................................... 114

6.7 3D mapping of FMC .................................................................................................... 116

6.8 Summary ...................................................................................................................... 120

Chapter 7. Integrating the 3D EWT estimates in radiative transfer modelling ............. 123

7.1 Introduction .................................................................................................................. 123

7.2 DART concept and background .................................................................................. 123

7.3 Parametrizing the model .............................................................................................. 126

7.4 Forest scene 1 ............................................................................................................... 128

7.5 Forest scene 2 ............................................................................................................... 133

7.6 Simulations .................................................................................................................. 135

7.7 Model validation .......................................................................................................... 137

7.8 Results and discussion ................................................................................................. 137

7.8.1 Comparing the simulated reflectance to Sentinel-2A data ................................... 137

7.8.2 Group 1 simulations ............................................................................................. 139

7.8.3 Effects of the woody materials on the plot reflectance ........................................ 140

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7.8.4 Effects of the understory on the plot reflectance ................................................. 140

7.8.5 Group 2 simulations............................................................................................. 141

7.9 Summary ..................................................................................................................... 144

Chapter 8. Discussion and conclusions .............................................................................. 147

8.1 Research motivation .................................................................................................... 147

8.2 Revisiting research aim and objectives ....................................................................... 148

8.3 Suggestions for future research directions .................................................................. 160

8.4 Conclusions ................................................................................................................. 170

References ............................................................................................................................. 173

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List of Figures

Figure 2-1. Typical leaf spectra in the visible, NIR, and SWIR regions of the electromagnetic

spectrum. .................................................................................................................................. 10

Figure 2-2. Multi-temporal change in CWC for 2005 for natural vegetated areas in the USA

generated from MODIS data. Figure was adapted from Trombetti et al. (2008). .................... 11

Figure 2-3. The effects of changing EWT on leaf spectra. Water absorption bands can be seen,

centred around 970, 1200, 1470, 1940, and 2500 nm. ............................................................. 12

Figure 2-4. NDI pointcloud derived from DWEL data in an open eucalyptus forest at

Tumbarumba, Australia, showing the distinctive difference between leaves (blue) and wood

(red). This figure was adapted from Newnham et al. (2015). .................................................. 29

Figure 3-1. The TLS instruments used in this research: (a) the Leica P20, (b) the Leica P40a

and P40b and (c) the Leica P50. ................................................................................................ 35

Figure 3-2. A 50% Spectralon panel scanned by the P20 and the P40a instruments: (a) the point

clouds, coloured in blue for the P20 and in red for the P40a and (b) a close up to show the point

distance between each point in the P20 point cloud and the corresponding point in the P40a

point cloud, with the average distance being 0.2 mm. ............................................................. 38

Figure 3-3. A flowchart of the TLS data processing pipeline used to generate 3D EWT point

clouds. ....................................................................................................................................... 38

Figure 3-4. Histogram of closest points within 3 cm after filtering a P20 point cloud and its

corresponding P40b point cloud, collected in Wytham Woods forest plot (Chapter 5). .......... 40

Figure 3-5. The wooden frame used in the leaf incidence angle experiment. .......................... 46

Figure 3-6. Leaf internal structure and cellular arrangements.................................................. 47

Figure 3-7. The intensity-range relationships for the four instruments used in this study. ...... 49

Figure 3-8. The P40b fitted polynomial functions for near ranges (red, 3rd degree function) and

for remaining ranges (blue, 6th degree function) with 5 m range chosen as the split point. ..... 50

Figure 3-9. The intensity-reflectance relationships of the instruments used in this study. ...... 52

Figure 3-10. The reflectance-incidence angle relationship for leaf samples for the 1550 nm

wavelength: (A1) group 1, (A2) group 2 and (A3) group 3; the reflectance-incidence angle

relationship for the 808 nm wavelength: (B1) group 1, (B2) group 2 and (B3) group 3, and the

NDI-incidence angle relationship: (C1) group 1, (C2) group 2 and (C3) group 3. .................. 56

Figure 3-11. (a) Effects of N on the leaf reflectance in the visible, NIR and SWIR regions of

the electromagnetic spectrum, and (b) effects of N on 808 nm wavelength, 1550 nm wavelength

and NDI. ................................................................................................................................... 57

Figure 3-12. (a) Effects of LMA on the leaf reflectance in the visible, NIR and SWIR regions

of the electromagnetic spectrum and (b) effects of LMA on 808 nm wavelength, 1550 nm

wavelength and NDI. ................................................................................................................ 57

Figure 3-13. Effects of leaf structure coefficient on the NDI – EWT relationship. ................. 58

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Figure 3-14. Effects of LMA on the NDI – EWT relationship. ............................................... 59

Figure 4-1. The trees involved in the indoors dry-down experiment: (a) the deciduous canopies

and (b) the coniferous canopies, while (1) indicates the control units and (2) indicates the dry-

down units. ............................................................................................................................... 62

Figure 4-2. Leaf level results of NDI against EWT for (a) Snake-bark maple leaf samples, (b)

the additional leaf samples and (c) all leaf samples combined. ............................................... 65

Figure 4-3. Leaf level results of NDI against EWT for the conifer samples. .......................... 65

Figure 4-4. For the deciduous canopies, (a) 3D EWT (g/cm2) distribution of the control unit

(left) and the dry-down unit (right) on day 8 and (b) the histogram of the EWT (g/cm2)

distribution for the dry-down and control units combined. ..................................................... 66

Figure 4-5. For the conifer canopies, (a) 3D EWT (g/cm2) distribution of the control unit (left)

and the dry-down unit (right) on day 9 and (b) the histogram of the EWT (g/cm2) distribution

for the dry-down and control units combined. ......................................................................... 66

Figure 4-6. The extracted woody materials, (a) the deciduous canopies on day 8 and (b) the

conifer canopies on day 9, many needles were incorrectly filtered as wood in the conifer dry-

down unit.................................................................................................................................. 67

Figure 4-7. NDI at canopy level against EWT of leaf samples for the dry-down units, (a) the

deciduous canopies and (b) the conifer canopies. The outlier on day 6 is included. ............... 68

Figure 4-8. Estimated EWT at canopy level with and without the woody materials against EWT

of leaf samples for the dry-down unit, (a) deciduous and (b) conifer. .................................... 68

Figure 4-9. The change in the estimated EWT from TLS measurements over the duration of the

experiment for the deciduous canopies. ................................................................................... 69

Figure 4-10. The change in the estimated EWT from TLS measurements over the duration of

the experiment for the conifer canopies. .................................................................................. 69

Figure 4-11. The vertical distribution of EWT for the dry-down units, (a) deciduous and (b)

conifer, after filtering the woody materials. ............................................................................. 70

Figure 5-1. The study area: (a) Wytham woods and the location of Wytham core plot and (b)

the treetop canopy walkway. .................................................................................................... 74

Figure 5-2. The 35 × 45 m rectangular plot and the thirteen sampled trees (indicated by numbers

assigned during fieldwork). Black indicates trees that were not sampled. .............................. 75

Figure 5-3. Example of using the mean (µ) of the fitted EWT histogram Gaussian distribution

to remove the noise by applying a threshold equal to 2µ (purple). .......................................... 79

Figure 5-4. The species-specific and pooled NDI – EWT relationships. ................................ 80

Figure 5-5. A boxplot of the EWT of the leaf samples in canopy top and canopy bottom layers:

(a) all leaf samples combined, (b) sycamore, and (c) oak. The whiskers are the minimum and

maximum values. ..................................................................................................................... 81

Figure 5-6. The EWT – LMA relationships at leaf level. ........................................................ 81

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Figure 5-7. The EWT – LMA relationships of the individual trees: (a) sycamore and (b) oak.

.................................................................................................................................................. 82

Figure 5-8. The relationship between EWT and LMA at canopy level: (a) all species combined,

(b) Sycamore and (c) Oak. ........................................................................................................ 82

Figure 5-9. The 3D EWT distribution of the sampled trees. .................................................... 83

Figure 5-10. Examples of the 3D EWT distribution of individual trees: (a) Sycamore tree,

labelled (6), and (b) Oak tree, labelled (11). ............................................................................ 83

Figure 5-11. The EWT vertical profiles. Tree (5) is beech, trees (1, 2, 3, 4, 6 and 12) are

sycamore and trees (8, 9, 10, 11 and 13) are oak. .................................................................... 86

Figure 6-1. The study area and the location of the 10 hectares devoted to the willow crops,

indicated by red. ....................................................................................................................... 90

Figure 6-2. The willow subplots and varieties, coloured by their harvested weight (Kg) in March

2015, after four years of growth. Shaded area (a) indicates the six willow subplots used in the

data collection. The figure was adapted from Gaulton et al. (2015). ....................................... 90

Figure 6-3. The willow varieties. All leaves were already senescent....................................... 91

Figure 6-4. A view of the three plots covered from scanning position one, left is plot ‘E’, middle

is plot ‘TE’, and right is part of plot ‘B’. Three out of four Leica black and white registration

targets can be seen, in addition to the reflectors used to mark the approximate boundaries of

each plot. ................................................................................................................................... 91

Figure 6-5. The P20 point clouds of scan position one (top) and scan position two (bottom).

The point clouds show only the front side of the plots, 1 m deep into the canopy. The colour

ramps show the uncalibrated intensity (dimensionless). .......................................................... 92

Figure 6-6. The relationship between NDI and EWT of each willow plot. ............................. 94

Figure 6-7. The relationship between the estimated EWT of the sampled layers and the actual

EWT: (a) for the variety-specific models and (b) for the pooled model. ................................. 97

Figure 6-8. Plot ‘T’ point clouds: (a) the P20 reflectance, (b) the P40 reflectance, (c) NDI point

cloud, (d) EWT point cloud, (e) woody materials extracted from the P20 reflectance point cloud

using 0.35 threshold and (f) woody materials extracted from EWT point cloud using 0.03 g/cm2

threshold. Thresholds were chosen by trial and error until changing the threshold did not

visually improve the results. ..................................................................................................... 98

Figure 6-9. The Exhibition Park dataset study area: (a) Exhibition Park and (b) the scanned tree

plot. ........................................................................................................................................... 99

Figure 6-10. Species-specific NDI – EWT relationships: (a) August and (b) October.......... 103

Figure 6-11. Pooled NDI – EWT model for August dataset. The trendline was affected by the

Sycamore low NDI values. ..................................................................................................... 104

Figure 6-12. Pooled NDI – EWT models for August (bottom), excluding the Sycamore leaves,

and for October (top). A shift can be seen between the two trendlines, caused by the leaf

senescence. ............................................................................................................................. 105

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Figure 6-13. Pooled NDI – EWT model for August leaf samples combined with leaf samples

collected in the indoor dry-down experiment (Chapter 4). Two of the diseased Sycamore leaves

appear as outliers, while the Holly leaf samples have their own EWT model. ..................... 106

Figure 6-14. Pooled NDI – EWT models for August (bottom) and for October (top), after

excluding Holly leaf samples. The shift in trendlines of the NDI – EWT relationships was

caused by the leaf senescence. ............................................................................................... 106

Figure 6-15. Pooled NDI – EWT models for October dataset (bottom), for willow dataset

(middle), and for willow plot ‘S’ and Holly leaf samples (top). ............................................ 107

Figure 6-16. Top view of the EWT point cloud of the plot in August and the approximate

boundaries of the trees: (a) Swedish Whitebeam tree 1, (b) Swedish Whitebeam tree 2, (c) Ash

tree 1, (d) Ash tree 2, (e) Beech tree, (f) Holly tree 1, (g) Holly tree 2, (h) Horse chestnut tree

and (i) Sycamore tree. * indicates that samples were collected form the tree for EWT validation.

................................................................................................................................................ 108

Figure 6-17. EWT pointcloud of Swedish Whitebeam tree 1 in August (left) and in October

(right). An increase in EWT was observed in October. ......................................................... 111

Figure 6-18. EWT vertical profiles in August and October: (a) Ash tree 1, (b) Ash tree 2, (c)

Swedish Whitebeam tree 1, (d) Swedish Whitebeam tree 2, (e) Holly tree 1 and (f) Holly tree 2.

................................................................................................................................................ 112

Figure 6-19. The general, species- and site-independent pooled NDI – EWT model that

combined all leaf samples. ..................................................................................................... 114

Figure 6-20. The NDI – FMC relationships at leaf level for October dataset leaf samples. . 117

Figure 6-21. 3D FMC point cloud of Swedish whitebeam tree 1. ......................................... 118

Figure 6-22. FMC vertical profile for six trees in the plot: (a) Swedish whitebeam trees 1 and 2,

(b) ash trees 1 and 2, and (c) holly trees 1 and 2.................................................................... 119

Figure 7-1. DART representation of Earth-atmosphere system. Figure is adapted from Gastellu-

Etchegorry et al. (2015). ........................................................................................................ 125

Figure 7-2. Pre-defined understory optical properties: (a) healthy grass, and (b) litter. ....... 127

Figure 7-3. Pre-defined bark-deciduous optical model used for woody materials. ............... 127

Figure 7-4. Creating 3D object for Sycamore tree 1 woody materials: (a) wood point cloud, (b)

outcome of applying Poisson surface reconstruction model and the histogram of point density

in mesh triangles, and (c) the final 3D object after removing the noise using the density

histogram, guided by visual inspection of the original point cloud. ...................................... 130

Figure 7-5. Creating 3D object for a leaf layer in Sycamore tree 1: (a) layer point cloud, (b)


in mesh triangles, and (c) the final 3D object after removing the noise. ............................... 131

Figure 7-6. The 3D tree objects used to build forest scene 1: (a) Sycamore tree 1, and (b) oak

tree 10. .................................................................................................................................... 132

Figure 7-7. 3D view of forest scene 1. ................................................................................... 133

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Figure 7-8. 3D view of forest scene 2: (a) woody materials 3D objects and (b) plot 3D objects,

combining wood and leaf 3D models. .................................................................................... 134

Figure 7-9. Comparison between the simulated NIR reflectance, SWIR reflectance, and NDWI,

and the actual mean values retrieved from Sentinel-2A satellite imagery of the plot. Whiskers

represent one standard deviation. ........................................................................................... 139

Figure 8-1. The NDI – EWT relationship for N = 1.5 (black), N = 2 (green), and N = 2.5 (blue),

resulted from PROSPECT simulations, in addition to the NDI – EWT relationship at canopy

level for all scanned trees in this study, excluding holly trees. .............................................. 151

Figure 8-2. Using NDI to detect diseased trees. The Sycamore tree, diseased with powdery

mildew (red), had significantly lower NDI than the healthy trees. ........................................ 154

Figure 8-3. The relationship between TLS estimated canopy EWT and actual canopy EWT

measured from destructive sampling for all trees involved in this study. .............................. 155

Figure 8-4. Retrieving other vegetation water status metrics and LMA from the 3D EWT point

cloud. ...................................................................................................................................... 162

Figure 8-5. The use of EWT vertical profiles generated from TLS to determine EWT reduction

coefficients that can be used to retrieve EWT of lower canopy layers from EWT of canopy top

layers, estimated from optical RS data. Additional layers can then be added to the 2D EWT

distribution map generated from the satellite imagery, with each layer representing EWT in a

lower canopy layer.................................................................................................................. 164

Figure 8-6. A flowchart of the LiDAR data processing pipeline to generate 3D EWT point

clouds in mixed-species sites using the general EWT estimation models derived from

PROSPECT simulations. ........................................................................................................ 167

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List of Tables

Table 2-1. The widely used vegetation moisture content estimation indices. .......................... 12

Table 3-1. Nominal specifications of the TLS instruments used in this research. ................... 36

Table 3-2. The TLS instruments usage in the data collection campaigns. ............................... 37

Table 3-3. Actual reflectance from Spectralon panels at P40 and P20 wavelengths. .............. 43

Table 3-4. Actual reflectance of the multi-step reference target used for the P50 calibration. 43

Table 3-5. Details of the range calibration experiments of the TLS instruments. .................... 43

Table 3-6. Details of the validation experiments of the TLS instruments. ............................... 44

Table 3-7. Actual reflectance of each of six panels in the multi-step painted board................ 45

Table 3-8. Properties of the polynomial functions for the calibration models. ........................ 50

Table 3-9. Errors in the reflectance estimation in the validation experiments. For the Spectralon

panels, Max error(a), average error(a) and RMSE(a) correspond to all ranges, while max error(b),

average error(b) and RMSE(b) correspond to ranges > 4 m. For the painted board, max error(a),

average error(a) and RMSE(a) correspond to all panels, while max error(b), average error(b) and

RMSE(b) correspond to panels with reflectance < 60%. ........................................................... 54

Table 5-1. Details of the species, locations and numbers of the leaf samples for the EWT

estimation validation. The samples from the ash tree, labelled 7, were excluded.................... 77

Table 5-2. EWT estimation errors in the twelve trees, for the canopy top and bottom layers. The

signs of the errors were ignored while calculating the average and total errors. ..................... 85

Table 6-1. Height and width of the scanned side of the plots. ................................................. 92

Table 6-2. The correlation between NDI and EWT. ................................................................ 94

Table 6-3. Slopes and intercepts of the NDI – EWT relationships. ......................................... 94

Table 6-4. Errors in the EWT estimation. Approach one refers to estimating EWT on a point-

by-point basis, while approach two refers to using the average NDI to estimate the average

EWT. ........................................................................................................................................ 96

Table 6-5. Leaf samples collected to build the EWT estimation model and validate the

estimation in August and October datasets. ........................................................................... 100

Table 6-6. The correlation between NDI and EWT for the species-specific models. ............ 102

Table 6-7. The errors in EWT estimations for the species-specific models, pooled model 1,

which refers to the pooled EWT model with holly leaf samples included, and pooled model 2,

which refers to the pooled EWT model without holly leaf samples. ..................................... 108

Table 6-8. Temporal changes in EWT between August and October. ................................... 111

xviii

Table 6-9. For August dataset, a comparison between the errors observed in the EWT estimation

using the all-samples pooled EWT model and the Park-only EWT model. .......................... 115

Table 6-10. For forest dataset, a comparison between the errors observed in the EWT estimation

using the all-samples pooled EWT model and the forest-only EWT model. ........................ 115

Table 6-11. The errors in the FMC estimations at canopy level. ........................................... 118

Table 7-1. DART default atmosphere geometry. ................................................................... 125

Table 7-2. PROSPECT parameters used to simulate leaf optical properties. ........................ 128

Table 7-3. LAI estimated from the trees 3D models.............................................................. 131

Table 7-4. The EWT vertical profiles for the 3D models of the Sycamore and Oak trees, used

to build forest scene 1. Height was measured to the centre of each layer. ............................ 132

Table 7-5. Group 1 simulations. EWT value was 0.014 g cm-2 for forest scene 2. ................ 135

Table 7-6. Group 2 simulations.............................................................................................. 136

Table 7-7. Group 1 simulations results. The change in reflectance and NDWI was calculated in

regard to Sim 1.1, except for Sim 1.5. ................................................................................... 139

Table 7-8. Group 2 simulated reflectance. The change in reflectance was calculated in regard

to Sim 1.4. Blue corresponds to changing EWT to 0.008 g cm-2, whilst grey corresponds to

changing EWT to 0.004 g cm-2. ............................................................................................. 142

Table 7-9. Group 2 simulated NDWI. The change in NDWI was calculated in regard to Sim 1.4.

Blue corresponds changing EWT to 0.008 g cm-2, whilst grey corresponds to changing EWT to

0.004 g cm-2............................................................................................................................ 142

Table 8-1. General EWT estimation models based on the PROSPECT simulations. ........... 166

xix

List of Abbreviations

3D Three Dimensions

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

AVIRIS Airborne Visible/Infrared Imaging Spectrometer

BOA Bottom Of Atmosphere

BRDF Bidirectional Reflectance Distribution Function

Cab Chlorophyll a and b content

Car Carotenoid content

Cb Brown pigment content

CESBIO CEntre for the Study of the BIOsphere from space

Cm Leaf dry matter content

Cw Leaf water content

CWC Canopy Water Content

DART Discrete Anisotropic Radiative Transfer

DW Dry Weight

DWEL Dual Wavelength Echidna LiDAR

E (%) Relative error

EWT Equivalent Water Thickness

FaNNI Foliage and Needles Naïve Insertion

FLIGHT Three-dimensional Forest Light Interaction

FLIM Forest Light Interaction Model

FMC Fuel Moisture Content

FRT Forest Reflectance and Transmittance

FW Fresh Weight

GVMI Global Vegetation Moisture Index

HA High Atmosphere

INFORM INvertible FOrest Reflectance Model

LA Lower Atmosphere

LAI Leaf Area Index

LIBERTY Leaf Incorporating Biochemistry Exhibiting Reflectance and Transmittance

Yields

LMA Leaf Mass per Area

LOPEX93 Leaf Optical Properties Experiment 1993

LUT Look-Up Table

xx

MA Mid Atmosphere

MIVIS Multispectral Infrared and Visible Imaging Spectrometer

MODIS Moderate-Resolution Imaging Spectroradiometer

MODTRAN MODerate resolution atmospheric TRANsmission

MSI Moisture Stress Index

N Leaf structure coefficient

NDI Normalized Difference Index

NDII Normalized Difference Infrared Index

NDVI Normalized Difference Vegetation Index

NDWI Normalized Difference Water Index

NIR Near InfraRed

QSM Quantitative Structure Model

RAMI RAdiation transfer Model Intercomparison

RMSE Root Mean Squared Error

RS Remote Sensing

RTM Radiative Transfer Model

RWC Relative Water Content

SA Surface Area

SAIL Scattering by Arbitrarily Inclined Leaves

SALCA Salford Advanced Laser Canopy Analyser

SLA Specific Leaf Area

SPOT Satellite for observation of Earth (Satellite Pour l’Observation de la Terre)

SPRINT Spreading of Photons for Radiation Interaction

SWIR ShortWave InfraRed

TLS Terrestrial Laser Scanning

TOA Top Of Atmosphere

UAV Unmanned Aerial Vehicle

VWC Vegetation Water Content

WI Water Index

xxi

1

Chapter 1. Introduction

1.1 Research context

Climate change has been linked to the recent increase in frequency and intensity of heatwaves,

with climate models predicting more heatwaves to occur in the future (Schär et al., 2004).

Heatwaves, when accompanied by lack of rainfall, can trigger severe drought conditions with

catastrophic effects on the agricultural and forestry sectors, reducing crop yields and increasing

rates of forest fires and tree mortality (Allen et al., 2015). For instance, the record-breaking

2003 European heatwave caused a fall in arable crop production by more than 10% (23 million

tons, the highest recorded drop in a century) in comparison to the previous year (García-Herrera

et al., 2010). In addition, more than 25,000 forest fires were reported across Europe, destroying

approximately 730,000 hectares of forests (García-Herrera et al., 2010). A total estimated loss

of approximately 13 billion Euros was reported, including losses in agricultural and livestock

sectors (García-Herrera et al., 2010). Stott et al. (2004) estimated that the risk of occurrence of

the 2003 European heatwave was doubled because of the increase in greenhouse gases

concentrations in the atmosphere caused by human activities. Similarly, Vogel et al. (2019)

rendered human-caused climate change as a factor that increased the magnitude of the 2018

European heatwave. Recently, the heatwave that hit Europe in June and July 2019 broke the

highest temperature records set by the 2003 heatwave (Mitchell et al., 2019).

During drought, plants use different survival mechanisms, one of which is closing leaf stomata

(pores on the underside of leaves) to minimize water loss, thus allowing less water evaporation

from leaves and reducing plant transpiration rate (Carter, 1993; Peñuelas et al., 1994; Ceccato

et al., 2001). Transpiration refers to water movement through a plant from roots to leaves, to

distribute water and nutrients needed for photosynthesis, before water gets evaporated through

leaf stomata (Jarvis and McNaughton, 1986). Stomatal closure further affects plant

photosynthetic rate by limiting carbon dioxide intake and exchange of gases with the

atmosphere (Farquhar and Sharkey, 1982; Chaves et al., 2003; Zivcak et al., 2013). The drop

in rates of transpiration, photosynthesis, and carbon gain cause a decline in the plant growth

rate and productivity (Lawlor and Cornic, 2002; McDowell et al., 2008; Mendiguren et al.,

2015), and it also becomes more prone to burning (Bartlett et al., 2016). If drought conditions

are prolonged, the plant may suffer from carbon starvation or hydraulic failure, eventually

leading to its death (McDowell et al., 2013; Sevanto et al., 2014). Continuous monitoring of

2

vegetation water status can lead to early detection of vegetation stress, which can help in

improving decision making during droughts, regarding crop irrigation and harvest scheduling

(Sepulcre-Cantó et al., 2006), and preventing and fighting forest fires (Yebra et al., 2008).

Furthermore, monitoring vegetation water status can help in the early detection of symptoms of

disease and signs of pest infestations in forests and agricultural crops (Carter, 1993; Ferretti,

1997; Jones and Tardieu, 1998; Datt, 1999; Meentemeyer et al., 2008; Trumbore et al., 2015;

Große-Stoltenberg et al., 2016).

A widely-used approach to determine vegetation water status and detect vegetation water stress

is measuring the water potential (Ψ) of leaf or stem, typically predawn, expressed in bar or

megapascal (Jarvis, 1976; Chone et al., 2001). Water potential is a direct measurement that can

be conducted on a plant in the field using a pressure chamber (Scholander et al., 1965). It is

directly linked to water movement through a plant from soil to foliage, and a drop in water

potential can indicate a water deficit, as it indicates that loss of water in transpiration exceeds

absorption of water via the roots (Jarvis, 1976; McCutchan and Shackel, 1992). However,

measuring the water potential with the pressure chamber is a slow process that can be

impractical if the aim is to determine the water status of a large number of plants (Vila et al.,

2011). Alternatively, thermal imagery can be used to estimate the water potential, as it can

detect the increase in canopy temperature caused by leaf stomatal closure during water stress,

which is inversely proportional to leaf water potential (Ehrler et al., 1978; Idso et al., 1981;

Vila et al., 2011).

Another approach to quantify water in vegetation is using vegetation water status metrics,

including leaf Equivalent Water Thickness (EWT), Fuel Moisture Content (FMC), Canopy

Water Content (CWC), Vegetation Water Content (VWC), and Relative Water Content (RWC).

EWT (g cm-2) is the amount of liquid water in a given leaf area (Danson et al., 1992), and is

widely adopted in vegetation health monitoring as it can reflect the physiological status of

vegetation and is related to the leaf tolerance to dehydration (Wright et al., 2004; Yilmaz et al.,

2008; Gaulton et al., 2013; Féret et al., 2018). FMC (%) is defined as the amount of liquid water

in a leaf divided by leaf dry weight (Burgan, 1996). It is a key metric in forest fire modelling

and is widely utilized in the early detection of wildfire risk (Danson and Bowyer, 2004; Aponte

et al., 2016; Zhu et al., 2017). CWC (kg m-2) is the mass of water in a canopy per unit ground

area (Clevers et al., 2010), and is a parameter of interest in studying the water cycle and its role

in global climate change (Clevers et al., 2010; Mendiguren et al., 2015). VWC (kg m-2) is the

total mass of water in leaves, branches, and stems, per unit ground area. VWC is used in

3

retrieving soil moisture content under vegetation canopies from active and passive microwave

remote sensing (Njoku and Entekhabi, 1996; Yilmaz et al., 2008). RWC (%) is the ratio

between liquid water volume in a leaf to maximum water volume when the same leaf is fully

saturated with water (Hunt Jr et al., 1987). RWC can reflect how a plant responds to water

stress, but is difficult to measure as it requires the measurement of leaf weight when the leaf is

fully saturated with water, which is hard to obtain in the field (Maki et al., 2004).

This research focuses mainly on EWT because it not only serves as a vegetation stress indicator,

but can also be used to retrieve other key vegetation water status metrics. EWT, when coupled

with canopy Leaf Area Index (LAI) measurements, the one-sided green leaf area per unit

ground surface area (Jonckheere et al., 2004), can be used to estimate CWC, expressed as EWT

multiplied by LAI (Clevers et al., 2010; Mendiguren et al., 2015). In the same manner, EWT

can be linked to FMC, expressed as EWT divided by Leaf Mass per Area (LMA) (Danson and

Bowyer, 2004). LMA (g cm-2) is the ratio of leaf dry weight to its surface area, and is an

important trait in plant growth rate (Gutschick and Wiegel, 1988; Poorter et al., 2009).

Furthermore, EWT can be used to estimate VWC using allometric relationships (Yilmaz et al.,

2008). Another important characteristic of EWT is that it is linked to water depth in the leaf,

thus can be estimated directly from reflectance in the optical domain, allowing the use of optical

Remote Sensing (RS) data in obtaining EWT estimates over large spatial scales (Ceccato et al.,

2002; Colombo et al., 2008).

Methods that utilize spaceborne and airborne optical RS data, both multispectral and

hyperspectral, to estimate EWT are considered a more efficient alternative to in-situ approaches

(destructive methods and field spectroscopy), which are time and effort consuming and

impractical for large areas (Pu et al., 2003; Dash et al., 2017). Such methods can not only

provide estimates of canopy EWT at a landscape level, but are also useful for producing time

series of data to monitor the change in vegetation moisture content (Foley et al., 1998; Colombo

et al., 2008; Clevers et al., 2010). EWT estimation from optical RS data is primarily based on

the interaction of radiation with foliage, with reflectance in the ShortWave InfraRed (SWIR)

region in leaf spectra being dominated by absorption by water (Knipling, 1970; Zarco-Tejada

et al., 2003). The two most common approaches to estimate EWT are using vegetation indices

or inversion of physical Radiative Transfer Models (RTMs) (Serrano et al., 2000; Mirzaie et

al., 2014). Vegetation indices combine the reflectance measured by the sensor in two or more

spectral bands (wavelengths) in a simple ratio or a Normalized Difference Index (NDI) and link

it to EWT using different types of regression analysis (Bannari et al., 1995; Jones and Vaughan,

4

2010). RTMs, on the other hand, simulate vegetation spectra at the leaf level, such as the

PROSPECT model (Jacquemoud and Baret, 1990), or at canopy level, such as the SAIL model

(Scattering by Arbitrarily Inclined Leaves) (Verhoef, 1984). By inverting these models, EWT

and other canopy biochemical characteristics can be estimated (Ceccato et al., 2002; Zarco-

Tejada et al., 2003; Mendiguren et al., 2015).

Estimating EWT from spaceborne and airborne optical RS data, despite its advantages over in-

situ approaches, has some limitations. Firstly, EWT can only be estimated at midday as the

sensors are dependent on the solar illumination (Eitel et al., 2010). Detecting the vegetation

water status at midday can be an unreliable indicator of water stress since leaves lose water

during photosynthesis, and it is therefore better to conduct the measurements predawn when

there is no transpiration (Améglio et al., 1999; Williams and Araujo, 2002). In addition, EWT

estimation from optical remote sensing data is affected by canopy structure, understory

vegetation and background soil reflectance, atmosphere, and shadows, as these factors affect

the canopy reflectance and the signal received by the sensor (Baret and Guyot, 1991; Zarco-

Tejada et al., 2003; Ali et al., 2016). Furthermore, the vertical heterogeneity in the canopy

biophysical and biochemical traits affects the light penetration and scattering within a canopy,

and thus plays a role in the canopy reflectance; a role that is often ignored because such

heterogeneity is difficult to measure and thus still needs to be investigated further (Valentinuz

and Tollenaar, 2004; Ciganda et al., 2008; Wang and Li, 2013; Liu et al., 2015). There is a

unique opportunity to address the aforementioned limitations using Terrestrial Laser Scanning

(TLS).

TLS instruments provide dense point clouds that include high-resolution information about the

structure of the scanned objects. As a result, TLS instruments have been widely utilized in

measuring vegetation canopy biophysical attributes, especially in forests, including but not

limited to: tree height, diameter at breast height, forest biomass, and canopy LAI (Takeda et al.,

2008; Ramirez et al., 2013; Calders et al., 2015). Furthermore, TLS point clouds include

intensity data in which the backscattered energy for each point is recorded, which can be linked

to scanned target reflectance (Penasa et al., 2014). However, radiometric correction is needed

for numerous factors that affect the TLS intensity data, including the instrumental effects, the

effects of the target distance, and the effects of the incidence angle of the laser beam: the angle

between the incident laser beam and the object’s surface normal (Kaasalainen et al., 2011;

Krooks et al., 2013; Tan and Cheng, 2016). Such effects have been highlighted in numerous

studies, and methods to calibrate the intensity to apparent reflectance have been successfully

5

developed for various TLS instruments (Höfle and Pfeifer, 2007; Jutzi and Gross, 2009;

Kaasalainen et al., 2011; Krooks et al., 2013; Blaskow and Schneider, 2014; Anttila et al., 2016;

Tan and Cheng, 2016; Zhu et al., 2017; Tan et al., 2018; Bolkas, 2019). The calibrated intensity

data can then be used to provide estimates of vegetation biochemical characteristics in three

dimensions (3D), if the TLS instrument operates at a suitable wavelength, such as the SWIR if

estimating EWT (Eitel et al., 2010; Gaulton et al., 2013; Magney et al., 2014).

One advantage of using TLS to estimate EWT is that the estimation can be carried out both at

midday and predawn, as TLS instruments are active sensors that are independent of the solar

illumination and cloud coverage. Another advantage is that the understory vegetation and soil

can easily be separated from the canopy in the point cloud, using the spatial positioning

information, thus removing their effect on the EWT estimation (Höfle, 2014). Furthermore, 3D

estimates of EWT enable the vertical heterogeneity in EWT within canopy to be studied,

including how it varies between species. In addition, more complex 3D RTMs that allow

representation of the heterogeneity in tree structure have been developed and validated, e.g.,

DART (Discrete Anisotropic Radiative Transfer) (Gastellu-Etchegorry et al., 1996; Demarez

and Gastellu-Etchegorry, 2000), SPRINT (Spreading of Photons for Radiation Interaction)

(Goel and Thompson, 2000), FLIM (Forest Light Interaction Model) (Rosema et al., 1992), and

FLIGHT (Three-dimensional Forest Light Interaction) (North, 1996), among others. Including

the 3D EWT estimates in RTMs can lead to a better understanding of how the EWT

heterogeneity affects canopy reflectance and received satellite signal.

There have been a few successful attempts in recent years to utilize TLS intensity data in the

estimation of EWT, using a single SWIR wavelength (Zhu et al., 2015; Zhu et al., 2017), or

NDI of two laser wavelengths (Gaulton et al., 2013; Junttila et al., 2016; Junttila et al., 2018;

Junttila et al., 2019). One advantage of using NDI over using a single SWIR wavelength is that

it does not require radiometric correction for the incidence angle effects, if the two wavelengths

involved in NDI were similarly affected (Eitel et al., 2014b; Hancock et al., 2017). Another

advantage is that NDI can be insensitive to leaf internal structure effects (leaf thickness and

LMA), while such effects can be significant when a single SWIR wavelength is used (Ceccato

et al., 2001). Although the aforementioned studies showed the potential of using TLS intensity

data in retrieving EWT, they investigated the relationship between TLS data and EWT at leaf

level only, or at leaf and canopy level for small individual trees in a controlled environment.

Only Junttila et al. (2019) recently used TLS to estimate EWT in a field campaign, but no 3D

EWT estimates were generated. There remains a gap regarding the use of TLS to retrieve 3D

6

EWT estimates in complex vegetation environments such as forests. In addition, methods to

model the EWT vertical heterogeneity in 3D RTMs are needed, as these models typically do

not account for the heterogeneity in vegetation biochemical traits within the canopy.

1.2 Research aim

The main aim of this research is to estimate EWT at leaf and canopy level in 3D using the NDI

of 808 nm Near InfraRed (NIR) wavelength (as utilized in Leica P20 commercial TLS

instruments) and 1550 nm SWIR wavelength (as utilized in Leica P40 and P50 commercial

TLS instruments), both in a laboratory setting and in multiple field campaigns (forest plot,

willow crop site, and urban tree plot). This necessitates the development of methods to calibrate

the intensity data from the different instruments used in the research to apparent reflectance,

and also the investigation into the ability of NDI to minimize the incidence angle and leaf

internal structure effects. Additional aims include: (1) investigating the potential of this EWT

estimation approach for detecting temporal changes in EWT, and (2) utilising the 3D EWT

estimates in the DART model to investigate how the vertical heterogeneity of EWT affects

forest plot reflectance and received satellite signal.

1.2.1 Research questions

Key research questions include:

1. Can intensity data from commercial dual-wavelength TLS be used to retrieve 3D

estimates of EWT at leaf and canopy level in complex vegetation environments?

2. Can the NDI of 808 nm and 1550 nm wavelengths minimize the incidence angle and

leaf internal structure effects without the need for further radiometric corrections?

3. How significant is the vertical heterogeneity of EWT within canopy and how does such

heterogeneity vary between species?

4. Can TLS detect temporal changes in EWT, and thus provide 4D EWT estimates?

5. Can the 3D EWT estimates be utilised in 3D RTMs to study how such heterogeneity

affects forest plot reflectance and received satellite signal?

1.2.2 Research objectives

The main research objectives are to:

7

1. Develop robust methods to calibrate the intensity data from commercially-available

TLS instruments to apparent reflectance.

2. Investigate the ability of NDI to minimize the effects of incidence angle and leaf internal

structure without the need for further radiometric corrections.

3. Examine the relationship between NDI and EWT at leaf level across a range of species.

4. Use NDI to generate 3D EWT estimates at canopy level in a controlled laboratory

experiment, as well as in field campaigns.

5. Study EWT vertical heterogeneity within canopy and determine how it varies across

different species and also between individual trees within each species.

6. Investigate the potential of using TLS to detect temporal changes in EWT due to drought

conditions.

7. Develop methods to utilise the 3D EWT estimates in the DART model and simulate the

effects of EWT vertical heterogeneity on satellite signal.

1.3 Thesis structure

This thesis consists of this introductory chapter and seven additional chapters. Chapter 2

reviews relevant literature in the field of estimating EWT using optical RS and TLS data,

discussing the advantages and limitations of such methods. Chapter 3 describes the different

TLS instruments utilized in this research, the research method, the intensity correction models,

and the incidence angle and leaf internal structure effects on NDI. Chapter 4 describes a dry-

down experiment conducted in a laboratory setting using four small trees from two different

species to investigate the ability of NDI to estimate EWT at leaf and canopy level in a controlled

environment. Chapter 5 describes the main data collection campaign conducted in a mixed-

species deciduous forest plot, aiming at estimating EWT in 3D in a real forest environment, and

addressing the issues associated with the process. Chapter 6 describes two additional data sets,

a willow crop plot and a mixed-species urban tree plot, with the first data set aiming at testing

the transferability of the presented method to a different site, and the second focusing mainly

on using TLS data to detect temporal changes in EWT. Chapter 7 describes methods to utilize

the 3D EWT estimates in DART, aiming at studying the effects of EWT vertical heterogeneity,

woody materials, and understory effects on forest plot reflectance. Finally, Chapter 8 presents

a general discussion and conclusion, and highlights the key findings of the research.

8

9

Chapter 2. Remote sensing of leaf equivalent water thickness

2.1 Introduction

This chapter reviews previous literature related to estimating EWT from optical RS data, using

vegetation indices and RTMs. Also, factors that affect the accuracy of the EWT estimation from

optical RS data are discussed, including the effects of the canopy biochemical and biophysical

traits vertical heterogeneity on canopy reflectance. Furthermore, the few previous studies that

have looked into the use of TLS in estimating EWT and other vegetation biochemical traits are

examined.

2.2 Measuring EWT

EWT can be measured on a small scale using in-situ approaches: destructive methods and field

spectroscopy. In destructive methods, leaf samples are collected and their Fresh Weight (FW),

Dry Weight (DW), and Surface Area (SA) are measured. EWT can then be expressed as follows

(Danson et al., 1992):

𝐸𝑊𝑇 (𝑔 𝑐𝑚−2) =𝐹𝑊 − 𝐷𝑊

𝑆𝐴 (2.1)

Field spectroscopy, using portable spectroradiometers, measures leaf reflectance and

transmittance and links them to leaf biochemical traits, such as EWT (Fourty and Baret, 1998).

This is based on the interaction of radiation with leaves being dependant on the biochemical

and biophysical characteristics of leaves (Jacquemoud and Baret, 1990) (Figure 2-1). Leaf

spectra in the visible region of the electromagnetic spectrum (350 – 700 nm), characterized by

low reflectance and transmittance, is mainly dominated by the influence of leaf pigments,

including carotenoids, chlorophyll, and brown pigments (Gausman, 1977; Jacquemoud and

Baret, 1990). Leaf internal structure dominates the spectra in the NIR region (700 – 1300 nm),

where leaf spectral response is characterized by high reflectance and transmittance (Gausman,

1977). In the SWIR region (1300 – 2500 nm), leaf water content is the main parameter that

influences the leaf spectral response (Tucker, 1980).

Measuring EWT using in-situ approaches is known to be time and effort consuming, in addition

to being impractical for large areas (Peñuelas et al., 1993; Pu et al., 2003; Dash et al., 2017).

An alternative approach to determine leaf EWT indirectly is by measuring leaf temperature,

assuming that the difference between air and leaf surface temperatures is caused by

transpiration, and thus can be linked to leaf water content (Chuvieco et al., 1999; Qi et al.,

10

2005). However, using such an approach at the canopy level has limitations, as canopy

temperature is not only affected by transpiration, but also by various metrological and

environmental conditions, such as wind speed, air temperature, and humidity (Leinonen et al.,

2006; Zhao et al., 2016). As a result of the limitations of the aforementioned approaches,

methods utilizing optical RS multispectral and hyperspectral data, airborne and spaceborne,

have been widely adopted in EWT estimation. Such methods are more efficient, cost-effective,

non-destructive, and can provide estimates of canopy EWT at a landscape level (Colombo et

al., 2008; Clevers et al., 2010; Wangab et al., 2015; Dash et al., 2017). It is worth mentioning

that radar remote sensing (wavelengths between 0.1 and 100 cm in the electromagnetic

spectrum) can also be used as a non-destructive approach to estimate vegetation moisture

content by measuring the leaf dielectric constant (leaf permittivity), which is directly

proportional to its moisture content (Moghaddam and Saatchi, 1999). However, this is outside

the scope of this research.

Figure 2-1. Typical leaf spectra in the visible, NIR, and SWIR regions of the electromagnetic

spectrum.

2.3 Estimating EWT from optical RS data

Optical RS sensors have a number of wavelength bands that record reflected energy in specific

sections in the electromagnetic spectrum, between 350 to 2500 nm, covering visible, NIR, and

SWIR regions. Multispectral sensors usually have three to 10 spectral bands, while

hyperspectral sensors can have dozens to hundreds of wavelength bands (Thenkabail et al.,

2015). Satellite sensors can provide EWT estimates at a landscape level and are very useful for

producing time series of comparable data (Foley et al., 1998). In addition, free access is

Visible

Leaf

Pigments

Near infrared

Cell structure

Shortwave infrared

Leaf water content

11

available to data from a number of the earth observation satellite sensors, such as Landsat,

MODIS (Moderate-Resolution Imaging Spectroradiometer), Hyperion and Sentinel-2, reducing

the cost needed for continuous monitoring of vegetation health. Figure 2-2 shows a time series

of the change in CWC for natural vegetated areas in the USA generated from MODIS data.

Figure 2-2. Multi-temporal change in CWC for 2005 for natural vegetated areas in the USA

generated from MODIS data. Figure was adapted from Trombetti et al. (2008).

Similar to field spectroscopy approaches, estimating EWT from optical RS data is primarily

based on the interaction of radiation with foliage in the SWIR wavelengths, being dominated

by absorption by water, where reflected energy is negatively related to leaf water content

(Knipling, 1970; Tucker, 1980; Faurtyot and Baret, 1997; Datt, 1999; Zarco-Tejada et al., 2003;

Féret et al., 2018). Figure 2-3 shows how the change in EWT mainly affects the leaf spectra in

the SWIR region, while having less effect on the NIR and no effect on the visible wavelengths.

However, SWIR reflectance alone is insufficient to accurately retrieve EWT, as leaf internal

structure and LMA also affect the SWIR reflectance (Ceccato et al., 2001) (See Section 3.6 for

more about the leaf internal structure effects). Combining NIR and SWIR reflectance in

vegetation indices can minimize the leaf internal structure effects and thus lead to a more

accurate estimation of EWT (Hunt and Rock, 1989; Ceccato et al., 2001; Ceccato et al., 2002).

12

In addition to vegetation indices, EWT can be estimated using inversion of physical RTMs

(Serrano et al., 2000; Yebra et al., 2013; Mirzaie et al., 2014).

Figure 2-3. The effects of changing EWT on leaf spectra. Water absorption bands can be seen,

centred around 970, 1200, 1470, 1940, and 2500 nm.

2.3.1 Vegetation indices

Vegetation indices link the reflectance measured by the sensor in two or more spectral bands

(wavelengths) to a specific vegetation biochemical trait, such as EWT, using different types of

regression analysis (Bannari et al., 1995; Jones and Vaughan, 2010). Table 2-1 shows the

widely used vegetation moisture content indices.

Table 2-1. The widely used vegetation moisture content estimation indices.

Index Formula Reference

Normalised Difference

Vegetation Index (NDVI) NDVI = (P858 – P648) / (P858 + P648)

(Rouse Jr et

al., 1974)

Normalized Difference

Infrared Index (NDII) NDII = (P820 – P1650) / (P820 + P1650)

(Hardisky et

al., 1983)

Normalised Difference

Water Index (NDWI) NDWI = (P860 – P1240) / (P860 + P1240) (Gao, 1996)

Water Index (WI) WI = (P900) / (P970) (Peñuelas et

al., 1993)

Moisture Stress Index

(MSI) MSI = (P1600) / (P820)

(Hunt and

Rock, 1989)

The Normalized Difference Vegetation Index (NDVI) (Rouse Jr et al., 1974), derived from the

reflectance in NIR and red wavelengths, mainly measures the vegetation greenness and

13

chlorophyll content, as red is a strong chlorophyll absorption region (Rouse Jr et al., 1974;

Tucker, 1979; Gao, 1996; Pettorelli et al., 2005). Based on the assumption that the change in

chlorophyll content is proportional to the leaf rate of drying and the change in leaf moisture

content (Paltridge and Barber, 1988; Illera et al., 1996), NDVI has been linked to vegetation

water status metrics, including FMC, EWT, and VWC (Peñuelas et al., 1994; Illera et al., 1996;

Sims and Gamon, 2003; Jackson, 2004; Hunt et al., 2017). However, this assumption cannot

be generalized to all species, limiting the use of NDVI in measuring vegetation moisture content

(Ceccato et al., 2001; Sims and Gamon, 2003; Chen et al., 2005). Combining NDVI with leaf

surface temperature measurements, derived from thermal imagery, was reported to lead to a

more accurate estimation of leaf moisture content than using NDVI alone (Alonso et al., 1996;

Chuvieco et al., 1999; Chuvieco et al., 2004b).

Hardisky et al. (1983) introduced the Normalized Difference Infrared Index (NDII), which is a

variation of NDVI that utilized SWIR reflectance instead of red, aiming at EWT retrieval from

Landsat data. Gao (1996) introduced a similar index, the Normalized Difference Water Index

(NDWI), which used a SWIR band centred at 1240 nm instead of 1650 nm as in NDII, as it has

atmospheric transmittance similar to the NIR band used in the index. Both indices have been

reported to be highly correlated to EWT (Maki et al., 2004; Cheng et al., 2008b; Colombo et

al., 2008; De Jong et al., 2014; Yi et al., 2014; Hunt Jr et al., 2016), and numerous variations

have been developed using different band combinations, e.g., (Ceccato et al., 2002; Fensholt

and Sandholt, 2003; Van Niel et al., 2003; Chen et al., 2005; Rodríguez-Pérez et al., 2007).

Hunt and Rock (1989) introduced the Moisture Stress Index (MSI), a simple ratio of reflectance

at 1600 nm and 820 nm, while Peñuelas et al. (1993) proposed the Water Index (WI), a simple

ratio of reflectance at 900 nm and 970 nm wavelengths. Both indices have also been

successfully used to retrieve EWT (Colombo et al., 2008; De Jong et al., 2014; Yi et al., 2014;

Liu et al., 2016; Neto et al., 2016).

Vegetation indices can easily be parameterized and applied, thus have been widely utilized in

estimating EWT at leaf and canopy levels. Ceccato et al. (2001) used the data of the Leaf

Optical Properties Experiment 1993 (LOPEX93) (Hosgood et al., 1995), in which leaf samples

from 50 species were collected in the area of Ispra, Italy, and their spectra and water content,

in addition to other biochemical traits, were measured, to study the relationship between MSI

and EWT, reporting high correlation (R2 = 0.92). Rodríguez-Pérez et al. (2007) reported a high

correlation (R2 > 0.91) between WI, calculated from in-situ spectroradiometer data, and EWT

of grapevines in commercial vineyards. De Jong et al. (2014) investigated the possibility of

14

estimating EWT for semi-natural vegetation and agricultural crops, including vineyards and

pasture, in the Peyne catchment in Mediterranean southern France, using WI and NDWI derived

from field spectroradiometer data. Good correlation was reported between EWT and the tested

vegetation indices (R2 > 0.7). Yi et al. (2014) reported high correlation between EWT and NDII,

NDWI, and WI (R2 > 0.9) in cotton plants during the growing season. Neto et al. (2016) studied

the relationship between WI and EWT of sunflower plants during a controlled dry-down

experiment for a duration of 12 days, reporting very high correlation (R2 > 0.94). Liu et al.

(2016) used destructive sampling and field spectroradiometer data to examine the relationship

between NDWI, NDII, WI, MSI, and EWT of winter wheat during the growing season,

reporting high correlation for all indices (R2 > 0.8). Hunt Jr et al. (2016) tested different

WorldView-3 NIR and SWIR band combinations and reported that the NDWI of 832 nm and

2165 nm wavelengths was highly correlated to EWT of leaf samples from various species.

Zhang et al. (2019) used the data of LOPEX93 to modify NDWI, and introduced a new

vegetation index constructed with 1725 and 2200 nm wavelengths, reporting that it was more

correlated to EWT than the original NDWI.

At the landscape level, Cheng et al. (2008b) used NDVI, NDWI, and NDII to retrieve canopy

EWT in sixteen plots of seven vegetation communities in south-eastern Arizona (grasslands,

shrublands, riparian grasslands, riparian cottonwood, riparian mesquite, oak woodlands, and

agriculture) from MODIS data. Due to the large pixel size of MODIS imagery (500 m × 500 m),

it was not possible to directly link the data to the EWT measured from leaf sampling in the

sixteen plots (40 m × 40 m). Thus, AVIRIS (Airborne Visible/Infrared Imaging Spectrometer)

data (20 m spatial resolution) was first linked to the ground truth data and EWT maps were

generated and validated with high accuracy (R2 = 0.9). For this, the leaf samples had to be

collected from canopy top layers. The AVIRIS EWT map was then degraded to MODIS 500 m

spatial resolution and used to retrieve and validate EWT from MODIS data. R2 ranged between

0.45 and 0.8 (R2 between 0.6 and 0.87 in the woodland plots). Colombo et al. (2008) estimated

EWT in 12 poplar plantation stands located on the floodplain of the Ticino River in Northern

Italy using numerous vegetation indices, including NDWI, NDII, and MSI, derived from

MIVIS (airborne Multispectral Infrared and Visible Imaging Spectrometer) data. Leaf sampling

was conducted on three trees from each stand, with the leaf samples being collected from the

upper canopy, and the total number of leaf samples being 144 leaves. The relative error in the

canopy EWT estimates was 20%.

15

Yilmaz et al. (2008) used NDII derived from Landsat-5 data to estimate canopy EWT in sites

of corn, soybean, and deciduous woodlands in Iowa, USA, reporting high correlation between

NDII and EWT (R2 = 0.85), and using the EWT estimates to retrieve the VWC. Landsat spatial

resolution allowed direct validation of the estimated EWT and VWC using destructive sampling

in 20 m × 20 m plots. However, the challenge of collecting leaf samples from the canopy top

in the woodland plots was highlighted. Zhao et al. (2016) used NDWI derived from MODIS

data to estimate canopy EWT in four 1 km2 winter wheat plots in Ningxia Plain, China. To

build the estimation model and validate the estimations, leaf samples were collected from upper

canopy layers from five locations in each 1 km2 plot, with each location consisting of ten trees.

The total number of leaf samples from each location was 100 leaves. EWT was measured and

the average EWT in each sampling location was considered the average of EWT of the leaf

samples collected. Good agreement was reported between estimated and measured EWT (R2 =

0.7). This study highlighted that using destructive sampling to fit EWT estimation models, and

to validate the estimation, for MODIS data can be very challenging, as it would require

collecting a large number of leaf samples from sampling plots, which have to be large enough

to match the MODIS pixel size.

Vegetation indices are sensor specific and dependant on site and sampling conditions, meaning

that an index developed using a specific data set may not be transferable to other sites or

applicable to different species (Tucker, 1980; Riaño et al., 2005; Yebra et al., 2008; Chuvieco

et al., 2009; Al-Moustafa et al., 2012; Yebra et al., 2013). At the landscape level, the

performance of vegetation indices is also influenced by canopy structure, understory vegetation,

soil moisture content, non-photosynthetic components, atmospheric conditions, solar

illumination and sensor viewing angles (Baret and Guyot, 1991; Serrano et al., 2000; Zarco-

Tejada et al., 2003; Eitel et al., 2010; Ali et al., 2016). Another limitation is that the indices not

only depend on the vegetation moisture content but also on other vegetation parameters

(Serrano et al., 2000). Zarco-Tejada et al. (2003) reported that NDWI was influenced, to an

extent, by leaf thickness, LMA and canopy LAI. Yi et al. (2014) and Zhang and Zhou (2015)

also reported that NDII, NDWI, and WI were affected by the canopy LAI. De Jong et al. (2014)

further highlighted that the accuracy of EWT estimation using vegetation indices was heavily

affected by canopy LAI.

Furthermore, at the landscape level, it is possible to directly link the vegetation indices

calculated from airborne and spaceborne optical RS data with high spatial resolution to canopy

EWT measured from leaf sampling conducted in sampling plots. However, leaves have to be

16

collected from the upper canopy layers, which can be challenging in forest plots. For spaceborne

sensors with low spatial resolution (large pixel size), such as MODIS, airborne imagery and/or

extensive destructive sampling is needed to scale the EWT estimates from plot level to

landscape level, and vegetation indices derived from the satellite imagery cannot be directly

linked to ground truth data.

2.3.2 Radiative transfer models

RTMs simulate vegetation spectra using the radiative transfer equation (Ross, 1981; Ross,

2012) and take the characteristics of leaf biophysical and biochemical traits, canopy structure,

and background soil into consideration. By inverting these models, EWT and other canopy

characteristics can be estimated (Jacquemoud et al., 1995; Ustin et al., 1998; Ceccato et al.,

2002; Zarco-Tejada et al., 2003; Kötz et al., 2004; Mendiguren et al., 2015). Another advantage

of RTMs over vegetation indices is that they are not site-dependant as they are based on a

physical principle, which means they can be applied at different locations and obtain good

results, as long as they accurately represent the vegetation canopy (Yebra et al., 2008; Yebra et

al., 2013; Quan et al., 2017).

Among the existing RTMs, the SAIL (Verhoef, 1984), PROSPECT (Jacquemoud and Baret,

1990; Jacquemoud et al., 2009), LIBERTY (Leaf Incorporating Biochemistry Exhibiting

Reflectance and Transmittance Yields) (Dawson et al., 1998), PROSAIL (Baret et al., 1992),

and GeoSail (Huemmrich, 2001) models are the most popular in retrieving biochemical and

biophysical characteristics of vegetation from optical RS data (Yebra et al., 2008; Zhang and

Zhou, 2015; Quan et al., 2017). PROSPECT simulates leaf optical properties for broadleaf

species at leaf level by parametrizing a number of leaf traits, including EWT, LMA, and

chlorophyll content, and modelling a leaf as a number of layers (plates), based on the plate

model introduced by Allen et al. (1969) (Jacquemoud and Baret, 1990). For more about

parameterizing PROSPECT, see Section 3.6. LIBERTY, on the other hand, was designed for

conifer needles and simulates needles’ spectra by modelling them as spherical cells separated

by airspace (Dawson et al., 1998). SAIL approximates the canopy as a turbid medium (a set of

infinitely small flat surfaces), assuming a homogenous and continuous canopy, then simulates

the canopy reflectance using canopy structural parameters, including LAI, leaf angle

distribution, and relative leaf size (Verhoef, 1984). PROSAIL links PROSPECT and SAIL to

describe the canopy reflectance as a function of leaf biochemistry and canopy structure

parameters (Baret et al., 1992). GeoSAIL combines SAIL with the Jasinski geometric model

(Jasinski and Eagleson, 1989), defining a vegetation plot as a number of trees modelled as

17

cylinders or cones distributed over a plane. GeoSAIL splits any scene into three components:

illuminated canopy, shadowed background, and illuminated background, aiming at simulating

canopy reflectance for discontinuous canopies (Huemmrich, 2001).

RTMs can be used to select suitable wavelengths for vegetation biochemistry estimation and

for developing and testing vegetation indices. For instance, Faurtyot and Baret (1997) used the

PROSPECT and SAIL models to determine which wavelengths were suitable for estimating

EWT, LMA, and LAI at canopy level from satellite data (900, 1100, 1190, 2040, and 2260 nm

for canopy EWT). Ceccato et al. (2002) used PROSPECT simulations to develop the Global

Vegetation Moisture Index (GVMI), a vegetation index optimized specifically to retrieve EWT

at canopy level from the SPOT-VEGETATION sensor (SPOT: Satellite for observation of

Earth, Satellite Pour l’Observation de la Terre). Sun et al. (2019) used PROSPECT simulations

and the LOPEX93 dataset to determine the suitable wavelengths to be utilized in multispectral

Lidar systems for EWT and chlorophyll content estimation, recommending the use of 680 nm

and 716 nm wavelengths for chlorophyll estimation and 1882 nm and 1920 nm wavelengths for

EWT.

Furthermore, RTMs were widely adopted in estimating canopy EWT at the landscape level

from optical RS data. Zarco-Tejada et al. (2003) found a high correlation (R2 > 0.7) between

EWT estimated from MODIS data, by inversion of the PROSPECT and SAIL models, and

actual EWT measured by destructive sampling in ten study sites of chaparral vegetation in

California, USA. The study also highlighted that NDWI was influenced by leaf LMA and

canopy LAI, and that inversion of RTMs, utilizing all MODIS bands, can minimize such effects,

making this method suitable for generating time series of canopy EWT, independent of seasonal

changes of LAI. Riaño et al. (2005) used the LOPEX93 dataset and inversion of PROSPECT

to estimate EWT and LMA, then to investigate the accuracy of estimating FMC from EWT and

LMA at leaf level, validating the estimates using destructive sampling of three trees (gall oak,

rosemary, and rock rose) in a laboratory experiment. EWT was estimated with high accuracy

(R2 = 0.94), while LMA was poorly estimated (R2 = 0.38), which affected the accuracy of FMC

estimation as a result (R2 = 0.33). PROSPECT was then linked to the Lillesaeter infinitive

reflectance canopy model (Lillesaeter, 1982) to estimate EWT and FMC at canopy level,

reporting that canopy EWT can be retrieved more accurately than canopy FMC (R2 = 0.75 and

0.62 respectively). This study further showed that inversion of RTMs can retrieve canopy EWT,

insensitive to leaf and canopy structure effects. Colombo et al. (2008) estimated canopy EWT

for 12 poplar plantation stands from MIVIS data by inversion of the PROSPECT and SAIL

18

models, validating the estimation using destructive sampling, and reporting a relative error of

27% in the estimation. This study highlighted that the successful estimation of canopy EWT at

landscape level using inversion of RTM requires prior knowledge of the model variables to

improve the performance of the model.

Trombetti et al. (2008) estimated canopy EWT in four sites in continental USA covering three

major vegetation types: woodlands, shrublands, and grasslands, from MODIS data by inverting

the PROSAIL model. Canopy EWT retrieved from AVIRIS data was used to calibrate the

model and validate the estimates, and good correlation was reported between canopy EWT

estimated from MODIS and AVIRIS data (R2 ranging between 0.57 and 0.71). The canopy

EWT estimated from MODIS was not validated directly using destructive sampling, as it was

considered extremely difficult and costly to collect a sufficient number of leaf samples in a

large number of sampling plots to directly scale the EWT measurements to 500 m MODIS

pixels.

Jurdao et al. (2013) inverted PROSPECT and GeoSAIL models to estimate canopy FMC from

canopy EWT using MODIS data in evergreen broadleaf, deciduous broadleaf, and coniferous

woodlands in Eurosiberian (energy-limited environment) and Mediterranean (water-limited

environment) regions in Spain. Leaf samples were collected from 19 sampling plots,

500 × 500 m to be representative of a MODIS pixel, and used in parametrizing the RTMs and

in validating the FMC estimates. Relative RMSE was reported to be < 30 %. Once again, the

challenge of collecting a sufficient number of leaf samples to represent EWT/FMC in large

sampling plots that matched MODIS pixel size was highlighted, and a total of 154 field

measurements, each consisting of 80 to 100 g of randomly-selected leaves, were used in this

study. Also, it was reported that collecting leaf samples from the canopy top was not always

possible, as not all trees were accessible.

Quan et al. (2017) showed that coupling different RTMs to invert Landsat 8 data to estimate

FMC from EWT and LMA, by using PROSAIL to simulate understory spectra, and GeoSAIL

to simulate canopy spectra, instead of using GeoSAIL alone, improved the accuracy of the

estimation (R2 = 0.86 and 0.74 respectively), in four study areas in Sichuan province, China.

The study areas consisted of evergreen broadleaf, deciduous broadleaf, and coniferous forests.

A total of 572 leaves samples were collected from the upper canopy in 41 plots, 40 m × 40 m

to be as close as possible to the pixel size of Landsat 8 (30 m × 30 m). FMC and EWT were

overestimated, and this was explained as a result of the number of leaf samples collected to

19

calibrate and validate the models being insufficient to represent all canopy properties in the

samples plots. This further highlighted the challenges associated with scaling up field

measurements to the landscape level. Zhu et al. (2019) inverted the INvertible FOrest

Reflectance Model (INFORM) to estimate canopy EWT in the southern part of the Bavarian

Forest National Park in Germany from airborne hyperspectral data, using canopy structural

parameters derived from airborne LiDAR data (Riegl LMS-Q 680i sensor) to parametrize the

model. Leaf samples were collected from the canopy top in 26 forest plots to calibrate and

validate the model. It was reported that using LiDAR data to parametrize the model improved

the canopy EWT estimation accuracy (R2 = 0.87 and 0.82 respectively).

One downside of using RTMs is the ill-posed nature of model inversion, that is, different

combinations of inputs can produce almost identical spectra (Combal et al., 2003; Baret et al.,

2007; Yebra and Chuvieco, 2009; Li and Wang, 2011). Solutions to this issue include the use

of a priori information about canopy characteristics to optimize the accuracy of RTMs

inversion, using look-up table based approaches (Weiss et al., 2000; Combal et al., 2003;

Darvishzadeh et al., 2008) or artificial neural network approaches (Baret et al., 1995; Atzberger,

2004). In addition, RTMs assume a homogenous canopy, using mean values of canopy

biophysical and biochemical traits to parametrize the model, which can lead to errors in the

simulated canopy reflectance in sites that exhibit large vertical and/or horizontal heterogeneity,

such as forests (Kuusk, 2001; Wang and Li, 2013). To overcome this limitation, Kuusk (2001)

developed a two-layer RTM that represents a vegetation plot as two different layers (a canopy

layer and an understory layer), allowing biochemical and biophysical traits to be assigned to

each layer separately, thus introducing some heterogeneity. However, the model did not account

for the horizontal heterogeneity, or for the vertical heterogeneity within the canopy.

More complex 3D RTMs have been developed, such as the DART (Gastellu-Etchegorry et al.,

1996; Demarez and Gastellu-Etchegorry, 2000), SPRINT (Goel and Thompson, 2000), FLIM

(Rosema et al., 1992), and FLIGHT models (North, 1996), among others. 3D RTMs, despite

being demanding in terms of computational time and the number of parameters that need to be

accurately defined, allow 3D realistic representation of vegetation canopies and account for the

horizontal and vertical heterogeneity in canopy structure. However, the models do not take the

vertical heterogeneity in canopy biochemical traits in consideration.

Overall, estimating EWT from spaceborne and airborne optical RS data, despite its advantages

over in-situ approaches, has some limitations. The temporal resolution (revisit time) of

20

spaceborne sensors may not be suitable for monitoring rapid changes in vegetation water status

(e.g. 16 days for Landsat, and 10 days for Sentinel-2 with one satellite, five days with two

satellites). Cloud coverage can also further reduce the temporal resolution, preventing usable

data collection on overcast days, and creating gaps in time series (White et al., 2014; Xue and

Su, 2017). The spatial resolution can also be a limiting factor in some applications, such as

precision agriculture and heterogeneous canopy areas where a single pixel may integrate

spectral response of numerous species and land cover types with different characteristics

(Eastman et al., 2013). Using data from very high resolution commercial sensors such as

WorldView-2 and WorldView-3 is not cost effective. On the other hand, optical and thermal

sensors mounted on Unmanned Aerial Vehicles (UAVs) can acquire data at very high temporal

and spatial resolutions, overcoming some of the limitations associated with spaceborne sensors

(Honkavaara et al., 2013; Ballesteros et al., 2015). However, some limitations associated with

UAVs include their limited coverage, short flying time, and payload and airspace law

constraints (Berni et al., 2009; Anderson and Gaston, 2013).

Estimating canopy EWT from optical RS data is also limited by the effects of canopy structure,

especially canopy LAI, and to reduce such effects, canopy EWT at landscape level is considered

the product of EWT and LAI. Calibrating and validating the EWT estimation models, whether

vegetation indices or RTMs are used in the estimation, requires collecting a large number of

leaf samples from the upper canopy layers in sampling plots that match the pixel size of the

spaceborne sensor. This is a challenging process, as the canopy top layers are not always

accessible, and the leaf samples collected from a sampling plot do not necessarily represent the

actual EWT in the whole plot. MODIS data is very popular in vegetation water status

monitoring because of the large coverage and high temporal resolution of MODIS. However,

scaling up the EWT measurements retrieved from destructive sampling to the landscape level

is even more challenging with MODIS data because of its large pixel size. The studies reviewed

in Section 2.3 showed that there is a need for a fast, non-destructive EWT estimation method

that can retrieve canopy EWT in large sampling plots and can be used in calibrating and

validating the EWT estimation models from spaceborne optical RS data. TLS can serve as such

a tool because of the high speed and long range of the commercially-available modern

instruments, making them suitable for collecting data at plot level. Also, TLS can easily provide

information about the canopy top layers, even if these layers are not accessible from the ground,

and EWT in such layers is crucial for calibrating and validating the EWT estimation models.

Furthermore, TLS can fill the gap in the time series of canopy EWT estimates derived from

21

satellite data, because TLS instruments are active sensors. In addition, TLS can provide

information about the vertical heterogeneity in canopy structure and biochemistry, which is

covered in Section 2.3.3.

2.3.3 Vertical heterogeneity in canopy biochemical traits

Leaves at different heights within a canopy contribute differently to the total canopy

photosynthesis, resulting in vertical heterogeneity in canopy biophysical and biochemical traits,

which can be linked to the canopy foliage-height profile (Aber, 1979; Ellsworth and Reich,

1993; Parker et al., 2001; Weerasinghe et al., 2014). Well-illuminated leaves in the canopy top,

known as sun leaves, usually have a higher photosynthetic rate than shaded leaves in the canopy

bottom, and plants tend to dedicate more nutrients and water to sun leaves to optimize

photosynthesis (Hirose and Werger, 1987; Hikosaka, 2004; Coble et al., 2016). The canopy

vertical heterogeneity affects the light penetration and scattering within the canopy, and thus

affects the canopy reflectance (Valentinuz and Tollenaar, 2004; Ciganda et al., 2008; Tang et

al., 2012; Wang and Li, 2013; Liu et al., 2015; He et al., 2016; Gara et al., 2018). Such vertical

heterogeneity has been recognized and highlighted in a number of studies.

Keating and Wafula (1992) found that a maize canopy has bell-shaped vertical distribution of

LAI, while for the same canopy type, Ciganda et al. (2008) reported that it also has bell-shaped

vertical distribution of chlorophyll content and leaf nitrogen content. Valentinuz and Tollenaar

(2004) reported that during senescence, which changes leaf internal structure (Jacquemoud and

Baret, 1990), leaves in the top and bottom canopy were the first to senesce, while leaves in the

middle canopy were the last. Koetz et al. (2008) reported that the vertical heterogeneity of CWC

can play a role in wildfire propagation. Tang et al. (2012) showed that canopy LAI varies

vertically, with LAI in the top of canopy being higher than that in the bottom of the canopy, in

a tropical rain forest. Liu et al. (2015) analysed the vertical distribution of EWT in winter wheat

and reported it to be a near bell-shaped curve with the highest values in the middle canopy

layers.

Zhu et al. (2017) reported that EWT varied vertically in 20 small trees from five different

species: Schefflera arboricola compacta (dwarf schefflera), Ficus benjamina twilight (weeping

fig), Ficus microcarpa (Chinese banyan), Ficus benjamina Danielle (ficus), and Zamioculcas

zamiifolia (Zanzibar gem), with EWT being higher in the top part of the canopy than the rest of

the canopy. This was explained as a result of new leaves at the top of the canopy having higher

water than older leaves (Mooney et al., 1977). Arellano et al. (2017) showed that canopy

22

biochemical and biophysical traits, including chlorophyll, EWT, and leaf thickness, varied

vertically within the canopy in three plots in the Amazon forest of Ecuador, with EWT and leaf

thickness being higher in canopy top layers, and chlorophyll being higher in middle canopy

layers. A possible explanation for the observations was the top layers of canopy receiving the

majority of irradiance (Chazdon and Fetcher, 1984). Ma and Upadhyaya (2018) reported

vertical heterogeneity in chlorophyll content and LMA in a tomato crop, showing that such

heterogeneity affected leaf spectra at different canopy heights. Gara et al. (2018) reported that

nitrogen, carbon, chlorophyll, EWT, and LMA were higher in the canopy top than in the canopy

bottom in four small trees (Camellia japonica, Ficus benjamina, Chamaedorea elegans, and

Fatshedera lizei).

Vegetation indices do not take into consideration the effects that vertical heterogeneity has on

canopy reflectance, which can influence their EWT estimation accuracy (Liu et al., 2015; Zhu

et al., 2017). RTMs assume that the canopy is vertically homogeneous in terms of the canopy

biochemical and biophysical properties, including LAI, chlorophyll and EWT, using an average

value to define each parameter in the model. The more complex 3D RTMs account for the

vertical heterogeneity in the canopy structure, but do not consider the vertical heterogeneity in

the biochemical characteristics, which can affect their canopy reflectance simulation accuracy

(Wang and Li, 2013). Such heterogeneity cannot be measured using optical RS spaceborne and

airborne data, as the reflectance measured by these sensors is dominated by the canopy top (Li

et al., 2014; Liu et al., 2015). Destructive sampling and field spectroscopy can provide some

information about the vertical distribution of canopy biochemical traits, by splitting the canopy

into multiple layers, typically upper, middle, and bottom canopy, and taking measurements in

each layer (Arellano et al., 2017; Gara et al., 2018). However, this is an impractical approach

for large areas and also cannot provide detailed vertical profiles of canopy biochemical traits,

which requires taking measurements in each canopy layer. TLS, being able to provide high-

resolution 3D information, can be a useful tool to measure such heterogeneity.

2.4 Terrestrial laser scanning

TLS instruments have the ability to determine the relative location of points in the surrounding

environment quickly and accurately. They record the backscattered laser beam energy and

measure the 3D coordinates of points on surfaces that reflected the laser signal. The 3D

coordinates are stored in dense point clouds that represent the 3D structure of the scanned

objects. In addition to the 3D coordinates of surrounding points, a point cloud includes an

intensity image in which the magnitude of the recorded backscattered energy for each point is

23

recorded. Available TLS instruments can be divided into three categories: time-of-flight

discrete pulse systems, continuous wave phase-shift systems, and time-of-flight full waveform

systems.

A time-of-flight discrete pulse scanner measures the time a pulse of light needs to travel to a

surface, interact with it and reflect back to the scanner (Cote et al., 2009). The instrument then

calculates the range to the target as follows:

𝑅𝑎𝑛𝑔𝑒 (𝑚) =𝑡 × 𝐶

2 (2.2)

Where t is the time of flight and C is the speed of light.

The range and the angle at which the light pulse was emitted are used to determine the 3D

coordinates of that specific point on the target’s surface in relation to the LiDAR scanner

position. Modern time-of-flight scanners have high accuracy and also long range. A phase-shift

scanner emits a sinusoidal laser pulse and determines the time of travel by calculating the phase-

shift between the emitted and returned pulse (Balduzzi et al., 2010). The instrument uses the

calculated time to measure the range to the target and determine the 3D coordinates of that

specific point on the target’s surface. Phase-shift scanners have a very high rate of points

measured per second. However, this is at the expense of the instrument’s range which is

relatively lower than that of discrete pulse time-of-flight scanners. A time-of-flight full

waveform scanner emits laser pulses, then records the full time trace of the energy of the

returned pulses, known as the full waveform (Lovell et al., 2011). The instrument then uses a

similar approach as the time-of-flight discrete return scanners to measure the range to the points

in the surrounding environment after post-processing the full waveforms. However, a major

difference from the discrete pulse scanners is that full waveform scanners record the full

intensity trace of the backscattered laser pulses. This leads to more information being available

from the point clouds, which can be useful in analysing very complicated scan scenes.

TLS instruments have been widely used for high resolution measurements of vegetation

canopies during the last two decades, especially in forests, allowing a comprehensive

description of forest biophysical characteristics (Henning and Radtke, 2006; Takeda et al.,

2008; Moskal and Zheng, 2011; Ramirez et al., 2013; Newnham et al., 2015; Disney, 2019).

Forest biophysical attributes obtained using TLS data include, but are not limited to, tree height

(Hilker et al., 2010; Olofsson et al., 2014; Laurin et al., 2019; Tian et al., 2019), stem diameter

(Lovell et al., 2011; Pitkänen et al., 2019), above ground biomass (Yu et al., 2013; Kaasalainen

24

et al., 2014; Srinivasan et al., 2014; Calders et al., 2015; Greaves et al., 2015), leaf area density

(Takeda et al., 2008), leaf area distribution (Béland et al., 2011), directional gap fraction

(Danson et al., 2007; Ramirez et al., 2013; Chen and Wang, 2016; Zheng et al., 2016), and

canopy LAI (Moorthy et al., 2008; Antonarakis et al., 2010; Zheng et al., 2013; Vincent et al.,

2015; Meng et al., 2019). Further applications include leaf – wood separation (Béland et al.,

2014; Douglas et al., 2015; Ma et al., 2016; Zhu et al., 2018), tree 3D modelling (Pfeifer et al.,

2004; Côté et al., 2009; Eysn et al., 2013; Hackenberg et al., 2015; Raumonen et al., 2015),

and realistic 3D forest plot reconstruction (Calders et al., 2016; Calders et al., 2018). The former

two applications allowed the realistic representation of trees in 3D RTMs (Widlowski et al.,

2015), which can improve the accuracy of their simulation of canopy reflectance (Zhu et al.,

2019). On the other hand, less attention has been paid to utilising intensity data recorded by the

TLS instruments to estimate important vegetation biochemical characteristics, such as EWT, at

leaf and canopy level.

2.4.1 TLS intensity data

TLS instruments not only provide information on canopy structure, but also record intensity

data that is a function of the reflectance of the scanned object (Penasa et al., 2014). Thus, by

using a shortwave infrared wavelength instrument, the recorded intensity can theoretically be

used to provide 3D estimates of EWT at leaf and canopy level (Gaulton et al., 2013; Zhu et al.,

2017). However, the scanned object reflectance is not the only parameter that influences the

TLS recorded intensity data. Numerous other factors influence the intensity, including

instrumental effects, and the scan geometry (Krooks et al., 2013; Kashani et al., 2015). Thus,

radiometric correction is needed prior to linking the intensity to EWT. The instrumental effects

refer to the instrument internal processing that alters the intensity values to enhance the

visualization of the point cloud and/or improve the range measurement using algorithms that

are unknown to the end-user (Danson et al., 2014). The scan geometry effects include the range

and the incidence angle effects. The laser equation (Höfle and Pfeifer, 2007), derived from the

radar equation (Jelalian, 1992), summarizes the aforementioned factors as follows:

𝑃𝑟 =𝑃𝑡 𝐷

2𝜌 cos (𝜃)

4 𝑅2 𝜂𝑠𝑦𝑠 𝜂𝑎𝑡𝑚 (2.3)

Where Pr is the received backscattered signal, Pt is the emitted signal, D is the receiver aperture

diameter, ρ is the target reflectance coefficient, θ is the incidence angle, ηsys corresponds to the

instrumental effects, ηatm corresponds to the atmospheric effects, and R is the range to target.

25

Parameters Pt, D and ηsys, all being sensor-related, and ρ, being a characteristic of the scanned

target, are theoretically constant during a single scan (Höfle and Pfeifer, 2007; Jutzi and Gross,

2009), while ηatm can be neglected for close-range TLS (Kaasalainen et al., 2011; Fang et al.,

2015). Thus, in a single scan, the scan geometry remains the major element affecting the TLS

recorded intensity (Krooks et al., 2013).

The range effect refers to how the distance between the instrument and the scanned object

affects the backscattered laser pulse magnitude, which is then recorded as intensity data

(Kaasalainen et al., 2011; Tan et al., 2016). Typically, the farther the laser pulse travels, the

more energy it loses, and the lower the recorded backscattered intensity will be, in comparison

to similar objects nearer to the instrument (Kashani et al., 2015). According to the laser

equation, the recorded intensity is inversely proportional to the square of the measured range.

However, numerous authors reported that this relationship is only theoretical and that the actual

relationship between intensity and range must be studied separately for each instrument.

Kaasalainen et al. (2011) showed that the intensity-range relationship for the FARO LS880 and

Leica HDS 6100 scanners only follow the 1/R2 relationship after 10 m. Blaskow and Schneider

(2014) reported high deviation in the intensity-range relationship from the 1/R2 prediction for

the Riegl LMS Z420i scanner, while the Z+F Imager 5006i scanner follows the 1/R2

relationship only after 5 m. Fang et al. (2015) reported the same observation for the Z+F Imager

5006i scanner. Tan et al. (2016) found a deviation from the laser equation at both near and far

ranges for the Faro Focus3D 120 scanner. The deviation in the intensity-range relationship from

the laser equation was explained to be a result of the instruments being equipped with a near-

distance intensity reducer to protect the optics and/or far-distance amplifiers to enhance the

range measurements for far targets (Jutzi and Gross, 2009; Kaasalainen et al., 2011; Blaskow

and Schneider, 2014; Fang et al., 2015; Tan et al., 2016; Calders et al., 2017). TLS instruments

accurately record the coordinates of each point in the point cloud, which can be used to calculate

the range of each point to be used in the radiometric calibration. Typically, external reference

targets with known reflectance can be used to develop the intensity correction models (Pfeifer

et al., 2008; Kaasalainen et al., 2009; Kaasalainen et al., 2011; Blaskow and Schneider, 2014;

Kashani et al., 2015).

The incidence angle of the laser beam is the angle between the incident laser beam and the

object’s surface normal (Kaasalainen et al., 2016). According to the laser equation, the TLS

recorded backscattered intensity is directly proportional to the cosine of the incidence angle for

a Lambertian surface (Kaasalainen et al., 2016). The effect of the incidence angle primarily

26

depends on the target surface characteristics (Tan and Cheng, 2016). However, Lambertian

cosine law (Equation (2.4)) can often sufficiently describe the incidence angle effect, whether

the scanned target can be approximated as a Lambertian surface or not (Kaasalainen et al.,

2011). This does not apply for surfaces with high irregularity (Krooks et al., 2013).

𝐼𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑒𝑑 = 𝐼𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡 cos 𝜃 (2.4)

Where Ireflected is the intensity reflected from the target’s surface and Iincident is the intensity

incident on the target’s surface.

Hancock et al. (2017) and Kaasalainen et al. (2018) showed the effects that incidence angle has

on TLS recorded backscattered intensity of leaf samples, which can cause a significant bias in

TLS estimation of leaf biochemical traits if no radiometric corrections are applied. To correct

for such effects using the Lambertian law, the incidence angle of the laser beam for each point

in the point cloud needs to be determined first. TLS instruments do not record the incidence

angles automatically and the incidence angle needs to be determined by the end-user. This is

achievable by fitting surface planes to the points and calculating the surface normal vector for

each point, then calculating the incidence angle using the normal vector and the point vector

between the point and the scanner. However, in a complex vegetation canopy, it is not trivial to

use such an approach to calculate the incidence angle for each leaf/needle in the point cloud

(Hancock et al., 2017), making it a challenge to accurately calibrate for the incidence angle

effect with a single wavelength (Béland et al., 2014). The approach is time and computational

resource consuming, plus, the accuracy of the normal vector estimation can be of concern,

especially at greater ranges where there might be insufficient points per leaf to fit a plane.

Alternatively, the Normalized Difference Index (NDI) of two laser wavelengths, typically NIR

and SWIR (Equation (2.5)), in the same context of NDWI and NDII used in optical RS data,

can be used to minimize the incidence angle effect (Hancock et al., 2012; Hancock et al., 2017).

Here the two wavelengths must be similarly affected by the incidence angle (Eitel et al., 2014b;

Hancock et al., 2017), or else radiometric correction will still be needed (Zhu et al., 2015).

𝑁𝐷𝐼 =𝜌𝑁𝐼𝑅 − 𝜌𝑆𝑊𝐼𝑅

𝜌𝑁𝐼𝑅 + 𝜌𝑆𝑊𝐼𝑅 (2.5)

Where ρNIR is the reflectance at a NIR wavelength, and ρSWIR is the reflectance at a SWIR

wavelength.

27

TLS instruments typically operate at a single wavelength, and despite successful attempts to

calibrate the intensity data to apparent reflectance for various TLS instruments (Höfle and

Pfeifer, 2007; Jutzi and Gross, 2009; Kaasalainen et al., 2011; Krooks et al., 2013; Blaskow

and Schneider, 2014; Anttila et al., 2016; Tan and Cheng, 2016; Zhu et al., 2017; Tan et al.,

2018; Bolkas, 2019), using a single wavelength TLS instrument to estimate EWT at the canopy

level remains a challenge as a result of the incidence angle effects. Another factor that

complicates the use of single wavelength TLS in EWT estimation is leaf internal structure

effects. Leaf thickness and LMA influence the shortwave infrared reflectance, and their

variation within canopy and between different trees in a vegetation plot can complicate the

retrieval of EWT (Ceccato et al., 2001). Also, the partial canopy hits (edge returns), which

occur when a leaf does not fully occupy the laser beam footprint, can affect the accuracy of the

TLS estimation of leaf biochemical characteristics (Eitel et al., 2010). Dual- and multi-

wavelength TLS systems, also known as multispectral and hyperspectral LiDAR systems, have

recently been developed in an attempt to overcome the limitations of single-wavelength TLS

(Danson et al., 2014).

2.4.2 Multispectral and hyperspectral LiDAR

Multispectral and hyperspectral LiDAR systems, typically non-commercial full-waveform

instruments, have been developed specifically for use in vegetation applications. As the

instruments record the full-waveform of the backscattered intensity, and thus can detect

multiple objects on the laser beam path, they are capable of providing more detailed information

about canopy structure, especially the canopy interior, than the commercial single-wavelength

pulsed TLS systems (Danson et al., 2014; Douglas et al., 2015). Furthermore, as they operate

at two or more wavelengths, spectral indices can be derived from the intensity data, similar to

vegetation indices derived from multispectral and hyperspectral optical RS data. As discussed

in Section 2.4.1, such spectral indices can minimize the incidence angle and leaf internal

structure effects. This allows utilizing the data to measure EWT and other vegetation

biochemical traits (Gaulton et al., 2013), and also allows better leaf – wood separation results

than using a single wavelength (Danson et al., 2018), which can lead to more accurate

estimation of forest above ground biomass.

A number of multispectral and hyperspectral LiDAR instruments have been developed in the

last decade, mainly for scientific research purposes. Chen et al. (2010) developed a prototype

dual-wavelength instrument, operating at 600 nm and 800 nm wavelengths, with the purpose

of deriving NDVI from the TLS data to distinguish between vegetation and non-living objects.

28

Wei et al. (2012) developed a four-wavelength instrument (555 nm, 670 nm, 700 nm, and 780

nm) to measure vegetation nitrogen and chlorophyll content. Hakala et al. (2012) developed a

hyperspectral TLS system that employs eight wavelengths between 500 nm and 1100 nm,

optimized for measuring vegetation biochemical traits. The study showed the potential of

deriving spectral indices from the TLS instrument data, including the modified chlorophyll

absorption ratio index, NDVI, and WI. Eitel et al. (2014b) introduced a dual-wavelength TLS

system that employed green (532 nm) and red (658 nm) wavelengths for leaf nitrogen content

estimation. Li et al. (2014) designed a hyperspectral LiDAR system with 32 wavelength bands

covering the range between 409 nm and 914 nm to estimate leaf chlorophyll, nitrogen, and

carotenoid concentrations. Sun et al. (2014) and Du et al. (2016) developed similar

hyperspectral LiDAR systems for the same purpose. Wang et al. (2018) developed an eight-

wavelength hyperspectral LiDAR instrument, covering the spectral range between 540 nm NIR

to 1460 nm SWIR wavelengths. The development of the instrument aimed at including SWIR

bands, as the majority of the available multi- and hyperspectral LiDAR systems operated at

visible and NIR wavelengths only. The aforementioned instruments have been mainly utilized

in lab or small-scale experiments.

Two dual-wavelength TLS instruments have been developed and then deployed, not only in lab

experiments but also in field campaigns: the Dual Wavelength Echidna LiDAR (DWEL)

(Douglas et al., 2015) and the Salford Advanced Laser Canopy Analyser (SALCA) (Danson et

al., 2014). DWEL was developed by the University of Boston, University of Massachusetts,

Boston, University of Massachusetts, Lowell, USA, and CSIRO, Australia (Douglas et al.,

2015), while SALCA was developed by the University of Salford, UK and Halo Photonics Ltd

(Danson et al., 2014). Both instruments are full waveform TLS systems, with SALCA operating

at 1064 nm NIR and 1545 nm SWIR wavelengths, and DWEL employing 1064 nm and 1548

nm wavelengths. Spectral indices calculated using NIR and SWIR wavelengths, such as NDI,

can minimize the incidence angle effects, as investigated and confirmed by Hancock et al.

(2017) using leaf samples from a variety of species. NDI was also found to be sensitive to EWT

(Gaulton et al., 2013). Thus, the instruments have been utilized for leaf-wood separation at

single tree and forest stand levels, as using spectral indices such as NDI made leaves

distinguishable from non-photosynthetic woody materials in the point cloud (Figure 2-4)

(Brodu and Lague, 2012; Douglas et al., 2015; Newnham et al., 2015; Danson et al., 2018; Li

et al., 2018).

29

Figure 2-4. NDI pointcloud derived from DWEL data in an open eucalyptus forest at

Tumbarumba, Australia, showing the distinctive difference between leaves (blue) and wood

(red). This figure was adapted from Newnham et al. (2015).

Despite their potential in estimating the vegetation biochemical and biophysical characteristics,

multispectral and hyperspectral TLS systems are still a proof-of-concept, not commercially

available, and need further development of data extraction algorithms to make full use of the

information they provide (Danson et al., 2014).

2.4.3 TLS and vegetation biochemical traits

A few recent successful attempts to utilize TLS intensity data to estimate vegetation

biochemical traits can be found in the literature. Eitel et al. (2010) and (2011) showed the

potential of using TLS intensity data to estimate vegetation biochemical characteristics at leaf

and canopy level. Green laser (532 nm) intensity, utilized in the Leica ScanStation-2 TLS

instrument, was found to be highly correlated (R2 = 0.77) to foliar chlorophyll content of bur

oak (Quercus macrocarpa) and sugar maple (Acer saccharum) leaf samples (Eitel et al., 2010),

and to total foliar nitrogen (R2 = 0.68) of spring wheat (Triticum aestivum L.) leaf samples (Eitel

et al., 2011). Wei et al. (2012) used four-band multispectral LiDAR to estimate nitrogen content

of leaf samples from seven different species, finding a strong relationship between three derived

laser intensity indices and foliar nitrogen content (R2 = 0.72, 0.78 and 0.82 respectively). Eitel

et al. (2014a) used a Leica ScanStation-2 TLS instrument (532 nm) to determine winter wheat

crop nitrogen status during early season growth. Eitel et al. (2014b) used a green (532 nm) and

red (658 nm) dual wavelength TLS and derived green-red intensity ratio spectral indices to

estimate foliar nitrogen content, achieving good accuracy (R2 = 0.71). Magney et al. (2014)

used 532 nm green wavelength utilized in the Leica ScanStation-2 TLS instrument to measure

xanthophyll pigments (xanthophylls are pigments that play a role in photosynthesis and in

autumn, when chlorophyll levels decline, give leaves their red, yellow, and brown colours) as

30

an early indicator of vegetation stress using leaves from five different plant species (winter

wheat (Triticum aestivum L.), bur oak (Quercus macrocarpa), paper birch (Betula papyrifera

Marshall), aspen (Populus tremuloides Michx.), and sunflowers (Helianthus annuus L.)).

Nevalainen et al. (2014) used an eight-channel multispectral LiDAR (500 to 1300 nm),

described in Hakala et al. (2012), to estimate chlorophyll concentrations in Scots pine shoots

with high accuracy (R2 = 0.88). Hakala et al. (2015) used the same instrument to derive spectral

indices to estimate chlorophyll content for pine plantation at leaf, branch and tree levels. Li et

al. (2016) used vegetation indices derived from a 32-channel multispectral LiDAR, described

in Li et al. (2014), to estimate leaf nitrogen content, chlorophyll content, and carotenoid content,

finding a strong relationship between the indices and the biochemical characteristics (R2 = 0.71,

0.83 and 0.77 respectively). Du et al. (2016) and (2018) used data from a 32-channel

hyperspectral LiDAR to derive spectral indices and estimate leaf nitrogen content of rice under

controlled lab conditions and four different levels of fertilization, reporting a strong correlation

(R2 = 0.75). Using the 32-channel hyperspectral LiDAR described in Sun et al. (2014), Sun et

al. (2018) reported that combining hyperspectral LiDAR data with inversion of PROSPECT

can improve the accuracy of retrieving leaf chlorophyll content.

A number of studies have successfully linked TLS intensity data to estimate EWT. Gaulton et

al. (2013) found a strong relationship (R2 = 0.80) between EWT of leaf samples from three

different species: Brassica oleracea (cabbage), Spathiphyllum (peace lily), and Fallopia

japonica (Japanese knotweed), and the NDI of 1064 nm and 1545 nm wavelengths employed

by the SALCA instrument. Zhu et al. (2015) used a RIEGL VZ-400 scanner (1550 nm

shortwave infrared) to investigate the relationship between EWT of leaf samples from eight

species and the instrument intensity data, reporting a strong correlation (R2 = 0.76). Zhu et al.

(2017) used data from the same instrument to retrieve the EWT vertical profiles for 20 plants

from five different species: Schefflera arboricola compacta (dwarf schefflera), Ficus

benjamina twilight (weeping fig), Ficus microcarpa (Chinese banyan), Ficus benjamina

Danielle (ficus), and Zamioculcas zamiifolia (Zanzibar gem), reporting a good relationship at

leaf level (R2 = 0.66) and at canopy level (mean error of 4.46%). Vertical heterogeneity in the

canopy EWT was also reported, as all trees had higher EWT in the canopy top than in the

canopy bottom. However, as a single wavelength was used, radiometric correction for the

incidence angle effects on the intensity data was needed, which was achieved using reference

targets with known reflectance.

31

Junttila et al. (2016) reported a very strong relationship (R2 = 0.93) between the NDI of red

(690 nm) and shortwave infrared (1550 nm) wavelengths, utilized in phase-based Leica

HDS6100 and FARO X330 instruments respectively, and EWT of 101 leaf and needle samples

from five different species: Tilia cordata L. (small-leaved lime), Betula pendula L. (silver

birch), Acer platanoides L. (Norway maple), Picea abies L. (Norway spruce), and Pinus

sylvestris L. (scots pine). The relationship between EWT of Norway spruce seedlings and the

NDI of 905 nm and 1550 nm wavelengths, utilized in the FARO S120 and the FARO X330

TLS instruments respectively, was investigated by Junttila et al. (2018) and a strong

relationship (R2 = 0.91) was reported. Junttila et al. (2019) used the same instruments to detect

Ips typographus L. (European spruce bark beetle) infestation symptoms in 29 mature Norway

spruce trees, successfully classifying the trees into three classes (no, low, and moderate

infestation levels) with overall accuracy of 66%. Higher accuracy was obtained (90%) when

the trees were classified into two classes only: infested or not infested. Additionally, FMC was

reported to be a better indicator of European spruce bark beetle infestation symptoms than

EWT.

Although the aforementioned studies demonstrated the potential of using TLS intensity data to

estimate vegetation biochemical traits, these studies, with the exception of Junttila et al. (2019),

investigated the relationship between TLS data and vegetation biochemical traits at leaf level

only, or at leaf and canopy levels in controlled environments. The multispectral and

hyperspectral TLS systems used in these studies can still be considered a proof-of-concept, and

are not commercially-available. Danson et al. (2014) highlighted that processing methods for

accurate calibration and registration of the data from such systems are still needed in order for

the data to be usable at larger scales. Furthermore, the majority of multispectral and

hyperspectral TLS systems employ wavelengths that cover the visible and NIR regions only,

making them more suitable for estimating leaf chlorophyll, nitrogen, and carotenoid contents.

Commercial single-wavelength TLS systems that utilize SWIR wavelengths can be suitable for

estimating EWT at leaf level, or at canopy level for small trees in laboratory settings. However,

using such systems to estimate EWT at the canopy level in complex vegetation environments

such as forests will be limited by the incidence angle and leaf internal structure effects.

Combining the data from two different commercially-available TLS systems, operating at NIR

and SWIR wavelengths, has the potential to minimize such effects, thus rendering this method

applicable in a real forest environment. However, accurate aligning of the point clouds from

the two different instruments can be challenging, and although Junttila et al. (2019) applied this

32

approach in a Norway spruce forest plot, no 3D EWT point clouds or EWT vertical profiles

were generated, most likely as a result of the complications associated with aligning the point

clouds and estimating EWT on a point-by-point basis. No successful attempts to utilize TLS

data to generate 3D EWT estimates at the canopy level in forest stands, tree plots, or agricultural

sites appear to have been reported in the literature to date.

2.5 Summary

This chapter has reviewed the use of optical RS data for estimating EWT, identified its

limitations, and investigated the potential of using TLS to address such issues. Previous studies

that used vegetation indices and/or RTMs to estimate EWT from optical RS data were

examined. This revealed that vegetation indices were highly correlated to EWT at leaf level,

and some studies showed that they can also be used to successfully retrieve canopy EWT at

landscape level. However, vegetation indices were reported to be sensor- and site-specific, and

their use cannot be generalized. For example, NDII was designed to retrieve EWT from Landsat

data, while NDWI was designed for MODIS data. Thus, using these vegetation indices to

retrieve EWT from other sensors required either modifying them to suit the spectral bands

utilized in that sensor, or designing similar indices specifically for the sensor. In addition,

numerous studies highlighted that the accuracy of use of vegetation indices for retrieval of

canopy EWT at the landscape level was highly influenced by canopy structure, especially

canopy LAI.

RTMs were reported to be able to overcome these limitations, as they are based on physical

principles and thus are transferrable to any vegetation site. RTMs also take the canopy structural

parameters into account, and numerous studies showed that inverting the models can

successfully retrieve canopy EWT at the landscape level, as a product of EWT and LAI.

However, some authors showed that the use of RTMs must be accompanied by some previous

knowledge of canopy biochemical and biophysical traits in the site of interest, in order to

improve the accuracy of the model inversion. Complex 3D RTMs have been developed to

account for the vertical and horizontal heterogeneity in canopy structure, instead of assuming a

homogenous canopy. However, the vertical heterogeneity in canopy biochemical traits,

including EWT, remained unaccounted for, with few studies attempting to measure it by

destructive sampling and taking spectroscopy measurements at multiple canopy layers. These

studies further showed that EWT, chlorophyll content, and nitrogen content exhibited vertical

heterogeneity that varied between species. The effect of such heterogeneity on the canopy

reflectance and on the accuracy of canopy EWT estimation still needs to be further investigated,

33

as the few studies found in the literature that looked into such effects reported that the vertical

heterogeneity should not be ignored as it highly influences the canopy reflectance (Kuusk,

2001; Wang and Li, 2013; Quan et al., 2017).

Another issue that was highlighted in the literature, associated with the retrieval of EWT from

optical RS data in general, was that collecting a sufficient number of leaf samples from the

canopy top layers in sampling plots that match the sensor’s pixel size to calibrate and validate

the estimation models can be very challenging. Two gaps were identified in the literature: (1) a

fast, non-destructive approach to estimate canopy EWT at plot level, and to provide EWT

estimates of the upper canopy layers, is needed, (2) a more practical method is needed to

measure the vertical heterogeneity in canopy biochemical traits in order to investigate its effect

on the canopy reflectance.

Previous studies have shown that TLS can provide accurate measurements of canopy structure,

but fewer studies have investigated using TLS intensity data to measure EWT and other

vegetation biochemical traits. This is a result of the limitations associated with using a single-

wavelength TLS instrument to estimate vegetation biochemical traits, with the two main issues

being correction of the intensity data for incidence angle effects and for the leaf internal

structure effects at the canopy level. To overcome these limitations, a number of multi- and

hyperspectral TLS instruments have been developed for scientific research and were

successfully used to estimate EWT, chlorophyll content, nitrogen content, and carotenoid

content. As these instruments are not available commercially, other studies used intensity data

from commercially available TLS instruments, typically by combining the data from two

different instruments into a spectral index, and successfully estimated EWT, chlorophyll

content, and nitrogen content. However, the number of studies that used TLS to estimate EWT,

and vegetation biochemical traits in general, is very low, and all the studies found in the

literature were conducted in laboratory conditions at leaf level only, or at leaf level and small,

individual canopy level. Only one recent study to date has used TLS to estimate EWT in a real

forest environment to detect signs of pest infestation, but no 3D EWT point clouds or vertical

profiles of EWT were generated. There remains a gap in the literature regarding the use of TLS,

especially commercial instruments, in the 3D estimation of EWT at the canopy level in

challenging vegetation environments, such as forest stands and agricultural sites, where the

canopy structure, wind, and partial canopy hits can greatly complicate the process.

34

35

Chapter 3. Combining TLS intensity data from different instruments in the

NDI

3.1 Introduction

Accurate estimation of EWT in 3D at the canopy level, using dual-wavelength terrestrial laser

scanning, depends on a number of factors. Firstly, the intensity data from the two instruments

need to be calibrated to apparent reflectance accurately. Secondly, the two wavelengths utilised

in the NDI must be affected similarly by the incidence angle so that combining them in the NDI

would minimize the incidence angle effects with no need for radiometric corrections. In

addition, NDI needs to be able to minimize the leaf internal structure effects, or else, radiometric

corrections will be needed for each wavelength separately. Finally, accurate alignment of the

point clouds from the two different instruments is required, so that NDI can be calculated

correctly on a point-by-point basis and 3D EWT point clouds can be generated with low errors.

This chapter is dedicated to address the aforementioned factors. Firstly, the TLS instruments

used in this study are described. Then, the method for combining the data from different TLS

instruments to generate 3D estimates of EWT is described. Afterwards, details of the calibration

work for each instrument are given. Finally, the ability of NDI to minimize the incidence angle

and leaf internal structure effects is investigated.

3.2 TLS instruments

Four different TLS instruments were used in this research, a Leica P20, two different Leica P40

instruments, named hereafter as Leica P40a and the Leica P40b, and a Leica P50 (Figure 3-1).

The Leica P40a and the Leica P40b were different instruments of the same model, manufactured

in two different years, 2016 and 2017. The specifications of each of the four instruments are

described in Table 3-1.

Figure 3-1. The TLS instruments used in this research: (a) the Leica P20, (b) the Leica P40a

and P40b and (c) the Leica P50.

(a) (b) (c)

36

Table 3-1. Nominal specifications of the TLS instruments used in this research.

Leica P20 Leica P40a and P40b Leica P50

Measurement type Time-of-flight Time-of-flight Time-of-flight

Wavelength 808 nm 1550 nm 1550 nm

Beam divergence 0.20 mrad 0.23 mrad 0.23 mrad

Beam diameter at exit 2.8 mm 3.5 mm 3.5 mm

Beam diameter at 10 m 4.8 mm 5.8 mm 5.8 mm

Beam diameter at 20 m 6.8 mm 8.1 mm 8.1 mm

Maximum range 120 m at 18%

reflectivity

120 m at 8%

reflectivity

180 m at 18%

reflectivity

270 m at 34%

reflectivity

120 m at 8%

reflectivity

270 m at 34%

reflectivity

570 m at 60%

reflectivity

1 km at 80%

reflectivity

Scan rate up to 1,000,000

points/second

up to 1,000,000

points/second

up to 1,000,000

points/second

Highest resolution

(point spacing) 0.8 mm at 10 m 0.8 mm at 10 m 0.8 mm at 10 m

The Leica P20, operating at 808 nm NIR wavelength, was involved in all the experiments and

data collection campaigns conducted in this research. The P20 wavelength lies in a region in

the leaf reflectance spectra that is known to be insensitive to change in EWT but sensitive to

changes in leaf structure (Hunt and Rock, 1989; Gao, 1996; Liu et al., 2014). The three other

instruments operate at 1550 nm SWIR wavelength, which is very sensitive to the change in

EWT (Gaulton et al., 2013; Junttila et al., 2016; Zhu et al., 2017). The data from the P20 was

combined in the NDI with the data from each of the three SWIR instruments, depending on

their availability, as shown in Table 3-2. The NDI of NIR and SWIR wavelengths was

previously reported to be very sensitive to the change in EWT (Hunt and Rock, 1989; Ceccato

et al., 2001; Gaulton et al., 2013), to be able to minimize the incidence angle effects (Hancock

et al., 2017), and to be insensitive to the leaf internal structure effects (Ceccato et al., 2001).

However, this has not been investigated for the two wavelengths involved in this study, the

808 nm and 1550 nm wavelengths.

37

Table 3-2. The TLS instruments usage in the data collection campaigns.

Data

collection

campaign

Indoors dataset,

Chapter 4

Forest dataset,

Chapter 5

Willow dataset,

Chapter 6

Park dataset,

Chapter 6

Instruments

used

Leica P20

Leica P40a

Leica P20

Leica P40b

Leica P20

Leica P40a

Leica P20

Leica P50

In addition to having suitable wavelengths for a NDI highly correlated to EWT, the instruments

have similar chassis and laser beam exit location (Figure 3-1). They use a similar scanning

mechanism, with the ability to capture up to 1,000,000 points per second. Thus, consecutive

scanning of a target with the P20 instrument, followed by one of the three SWIR instruments,

mounted on the same tripod and occupying the same survey station, can result in very similar

scan geometry. Although Table 3-1 shows some differences in beam diameter at exit and beam

divergence between the P20 instrument and the SWIR instruments, the aforementioned

similarities and scan setup can lead to achieving sufficient overlap between the laser beams.

This can lead to a high registration accuracy of the point clouds from the different instruments,

which is necessary for estimating EWT on a point-by-point basis.

3.3 TLS data processing to generate 3D EWT point clouds

3.3.1 Scan setup

Combining the intensity data from the P20 and any of the three SWIR instruments required the

correct scan setup to take advantage of the similarities between the instruments, to achieve the

highest possible overlap between the point clouds. For this, a tripod was fixed on a survey point,

and the P20 was mounted on the tripod to scan the object of interest, being a single tree or a

group of trees. Afterwards, the P40a, the P40b or the P50 was mounted on the same tripod to

scan the object of interest, adopting the same scanning resolution used with the P20. The tripod

was then moved to the next survey point and the process was repeated. A minimum of three

Leica black and white registration targets were placed around the object of interest, at different

heights, in order to link each pair of scans.

Figure 3-2 shows a 50% SphereOptics Spectralon panel scanned indoors by the P20 instrument,

followed by the P40a instrument, using the aforementioned scan setup and scanning resolution

(point spacing) of 0.8 mm at 10 m. The average distance between the points from the two point

clouds, calculated using the cloud to cloud distance compute module in CloudCompare v. 2.6.2

software, was 0.2 mm. For the tree canopies scanned indoors (Chapter 4), this distance was

38

found to be 1.3 mm, while it was found to be 5 mm for the scans conducted in a real forest

environment (Chapter 5).

Figure 3-2. A 50% Spectralon panel scanned by the P20 and the P40a instruments: (a) the point

clouds, coloured in blue for the P20 and in red for the P40a and (b) a close up to show the point

distance between each point in the P20 point cloud and the corresponding point in the P40a

point cloud, with the average distance being 0.2 mm.

Processing the data to generate the 3D EWT point cloud followed the steps shown in Figure

3-3.

Figure 3-3. A flowchart of the TLS data processing pipeline used to generate 3D EWT point

clouds.

3.3.2 Point cloud registration

The point clouds were extracted from each instrument using Leica Cyclone 9.1 (Leica

Geosystems HDS) software. To reduce the number of partial hits (edge returns) in the point

39

clouds, the mixed pixel filter in Leica Cyclone was used on medium (default) setting. The filter

searched for points that had a measured range that was actually a mixture of various observed

ranges. The filter then disregarded these points, as they occurred when the edge of the object

partially occupied the laser footprint.

Each scanner had its own local coordinate system, thus, the point clouds needed to be aligned.

This was done in Leica Cyclone using the registration targets placed in the scans. The point

cloud from the SWIR instrument was considered as the reference in the registration process.

The Cyclone registration module detected the registration targets that co-existed in both scans

and used them as constraints to derive a transformation matrix. For this, the targets needed to

be labelled correctly while collecting the data so that the registration module could recognize

them and use them to link the scans. The transformation matrix was then applied to the P20

point cloud to align it to the corresponding SWIR point cloud. The accuracy of the registration

was evaluated and reported by Leica Cyclone in a form of Root Mean Squared Error (RMSE).

The registered point clouds were then exported as ASCII files for further processing. The next

step was to separately calibrate the intensity from each instrument to apparent reflectance. This

is described in details in Section 3.4 in this chapter. All of the processes described in the

subsequent sections were implemented in MATLAB (The MathWorks Inc., USA, 2016).

3.3.3 Point matching and NDI calculation

Despite the similarities between the instruments, resulting point clouds did not have the same

number of points. A P20 point cloud had more points than a corresponding point cloud collected

by the P40a, the P40b or the P50 instruments, because of the presence of different number of

partial hits and the slight differences in laser beam footprint and beam divergence. To account

for this, two sets of point cloud filtering were applied as follows:

(1) The P20 point cloud was filtered to match the same number of points in a corresponding

SWIR point cloud. For this, a 3D nearest neighbour function was applied in MATLAB, using

the SWIR point cloud as a reference. The function used an exhaustive search algorithm to

compute the distances in 3D between each point in the SWIR point cloud and all the points in

the corresponding P20 point cloud. The function then generated an index matrix that defined

the nearest neighbour in the P20 point cloud to each point in the SWIR point cloud. The index

matrix was then used to filter the P20 point cloud, retaining only the nearest neighbour points

and disregarding the remaining points, and generating a conjugate point cloud containing the

same number of points as the corresponding SWIR point cloud.

40

(2) A threshold of 3 cm was applied, that is, any pair of neighbour points > 3 cm apart was

removed from the P20 point cloud and the corresponding SWIR point cloud. This was required

because the nearest neighbour function tried to find the nearest neighbour in the P20 point cloud

to each point in the SWIR point cloud, regardless of how far that nearest neighbour was.

The 3 cm threshold was chosen on the basis of 97% of the nearest neighbour distances

being < 3 cm apart, while 99% of distances were < 6 cm apart. Applying the threshold

disregarded points from both the SWIR and P20 point clouds. Figure 3-4 shows a histogram of

closest points within 3 cm after applying the two sets of filtering to a P20 point cloud and its

corresponding P40b point cloud, collected in Wytham Woods forest plot (Chapter 5).

Figure 3-4. Histogram of closest points within 3 cm after filtering a P20 point cloud and its

corresponding P40b point cloud, collected in Wytham Woods forest plot (Chapter 5).

NDI was then calculated on a point-by-point basis (Equation (2.5)) to generate the NDI point

cloud.

3.3.4 Building the NDI – EWT estimation model

After generating the NDI point cloud, an NDI – EWT relationship was needed to convert the

NDI point cloud to 3D EWT point cloud. For this, leaf samples were collected from the scanned

trees in the data collection site. The number and species of the samples depended on the

experiment. The leaf samples were labelled and the fresh weight of each sample was measured,

immediately on collection, using a precise scale (0.001 g division). Afterwards, the leaf samples

were suspended in a wooden frame with thin black threads, positioned at a fixed distance from

41

a tripod. The leaves were scanned by the P20 instrument, followed by the SWIR instrument

used in the experiment. Throughout the scans, the vertical and horizontal incidence angle effects

were minimized by ensuring the wooden frame was as normal as possible to the laser beam

direction. The intensity values of each leaf sample were then extracted from the scans and

calibrated to apparent reflectance, using the intensity correction models described in Section

3.4. The NDI of each leaf was calculated according to Equation (2.5).

Next step was to calculate the EWT of each leaf sample. For this, the surface area of each leaf

sample was obtained using Image-J 1.50i software (Schneider et al., 2012) after dividing them

into groups and scanning each group and a scale with an Epson Perfection photo scanner.

Afterwards, the leaves were dried in an oven for 48 hours at 60° Celsius until no change in

weight was recorded. The dry weight of each leaf was measured using the same precise scale

used to obtain the fresh weight. The EWT of each leaf sample was calculated following

Equation (2.1).

NDI values of the leaf samples were plotted against the corresponding EWT values and reduced

major axis regression was used to determine the NDI – EWT relationship. Reduced major axis

regression was used instead of least squares regression because the latter assumes that only the

dependent variable in the regression (EWT in this case) is subject to errors. Least squares

regression then attempts to minimize the sum of squared errors of the vertical distance between

the actual dependent variable values (y values) and their corresponding predictions. However,

as both NDI and EWT were subject to errors, which violated the least squares regression

assumptions, reduced major axis regression was considered a more suitable approach. Reduced

major axis regression is a method specifically formulated to account for errors in both the

dependent and independent variables by attempting to minimize both vertical and horizontal

distances between data points and their predicted values (Smith, 2009). Furthermore, unlike

least squares regression, reduced major axis regression is symmetric, meaning that the resulting

regression equation can be back solved to predict the dependent variable from the independent

variable if needed.

3.3.5 Generating the EWT point cloud

The NDI – EWT relationship derived at leaf level was then applied on a point-by-point basis to

the NDI point cloud and the EWT point cloud was generated. Further processing was needed

to remove the woody materials and noise, which depended on the type and size of dataset and

thus is described in detail in the corresponding data collection chapters.

42

3.4 TLS intensity data calibration

As discussed in Section 2.4.1, the range and incidence angle are the two main parameters that

influence the TLS recorded backscattered intensity data. This section is dedicated to describing

the range effect calibration models, while the incidence angle effects are covered in Section 3.5.

3.4.1 Concept of range effect intensity calibration models

The aim of the range effect calibration model was to calibrate the instrument’s recorded

intensity to reflectance, as close as possible to the scanned target’s apparent reflectance,

regardless of how far the target was from the scanner. The calibration model thus conducted

two tasks: 1) calibrating the intensity data for the range effect and 2) transforming the calibrated

intensity to apparent reflectance. To achieve the first task, the intensity-range relationship for

each instrument was investigated by scanning a reference target with known reflectance at

various ranges from the scanner. The target’s intensity at each range was extracted and plotted

against the corresponding range to obtain the intensity-range curve. Polynomial functions were

then fitted to the curve and considered a reference for the range calibration model. The next

step was to choose an intensity value at any range to serve as a reference intensity for the

calibration model (Iref), to which the intensity values at the remaining ranges were calibrated.

To achieve the second calibration task, the intensity-reflectance relationship for each instrument

needed to be determined at the range of Iref. This was achieved by scanning reference targets

with different apparent reflectance, or a multi-step reference target, at Iref range. The targets’

intensity values were then plotted against the targets’ known reflectance to obtain the intensity-

reflectance relationship of the instrument. The intensity-reflectance relationship was used to

transform the calibrated intensity at all ranges to apparent reflectance.

In this research, five SphereOptics Spectralon panels, almost ideal Lambertian surfaces

according to the manufacturer, were used to build and validate the calibration models of the

P20 instrument, the P40a instrument and the P40b instrument. The nominal and true reflectance

of the panels are shown in Table 3-3. The calibration experiments for the P50 instrument were

conducted using a multi-step SphereOptics Zenith Lite Diffuse reflectance target (Table 3-4),

as the Spectralon panels were not available. Throughout all the calibration experiments

described in the upcoming sections of this chapter, the scanner’s laser beam exit location

approximately faced the centre of the panel, while the surface of the panel was almost

perpendicular to the laser beam direction. This aimed at minimizing the incidence angle effects,

so that the backscattered intensity would be affected by the range only.

43

Table 3-3. Actual reflectance from Spectralon panels at P40 and P20 wavelengths.

Spectralon 5% 20% 50% 90% 95%

P40 (1550 nm) 4.7% 24.2% 41.8% 90.2% 94.7%

P20 (808 nm) 4.5% 22.6% 43.5% 92.2% 96.1%

Table 3-4. Actual reflectance of the multi-step reference target used for the P50 calibration.

Panel 5% 20% 50% 90%

P50 (1550 nm) 5.5% 20.5% 47.7% 91.8%

3.4.2 Determining the intensity-range relationships

The intensity-range relationship was investigated separately for each scanner. A range

calibration experiment was conducted indoors for each instrument, by mounting a reference

target on a tripod and scanning it at various ranges. Table 3-5 shows the details of the calibration

experiment of each instrument.

Table 3-5. Details of the range calibration experiments of the TLS instruments.

Instrument Reference target Start range End range Step Iref range

Leica P20 50% Spectralon

panel 2 m 36 m 1 m 15 m

Leica P40a 50% Spectralon

panel 2 m 36 m 1 m 18 m

Leica P40b 50% Spectralon

panel 2 m 36 m 1 m 20 m

Leica P50 50% panel* 2 m 22 m 1 m 15 m

* Part of a multi-step panel.

3.4.3 Determining the intensity-reflectance relationships

An individual experiment was conducted for each instrument to determine the intensity-

reflectance relationship. The five Spectralon panels described in Table 3-3 were scanned

consecutively using the P20, the P40a and the P40b instruments at Iref range from the scanner

(Table 3-5). For the P50 instrument, the multi-step reference panel (Table 3-4) was scanned at

Iref range (15 m) away from the scanner.

3.4.4 Validating the intensity calibration models

For each instrument, an independent reference target was scanned indoors at various ranges, as

shown in Table 3-6. The developed calibration models were used to calibrate the intensity

values at each range for the range effect and to apparent reflectance. The accuracy of the models

were evaluated by comparing the estimated reflectance at each range to the reference target’s

44

true reflectance. The relative error (E %, Equation (3.1)) and the RMSE (Equation (3.2)) were

calculated. For the P20, two different validation experiments were conducted, one prior to the

indoors experiment (Chapter 4), and the other before the forest data collection campaign

(Chapter 5). This aimed at a direct comparison between the accuracy achieved using the P20

and that achieved using the corresponding P40 that was to be used in the data collection.

Table 3-6. Details of the validation experiments of the TLS instruments.

Instrument Reference target Start range End range Step

Leica P20 90% Spectralon panel 2 m 15 m 1 m

Leica P20 20% Spectralon panel 2 m 15 m 1 m

Leica P40a 90% Spectralon panel 2 m 18 m 1 m

Leica P40b 20% Spectralon panel 2 m 22 m 1 m

Leica P50 20% panel* 2 m 22 m 1 m

* Part of a multi-step panel.

𝐸 % = (�̂� − 𝑦

𝑦) × 100 (3.1)

𝑅𝑀𝑆𝐸 = √∑ (�̂�𝑖 − 𝑦𝑖)2𝑛

𝑖=1

𝑛 (3.2)

Where �̂� and 𝑦 are the estimated and true values respectively.

Prior to carrying out the main data collection campaign in a forest plot in Wytham Woods,

Oxford, UK (Chapter 6), additional validation experiments were conducted for the two

instruments that were to be used in the data collection, the P20 and the P40b instruments. The

additional experiments were conducted using a wooden multi-step painted board. The board

was 0.75 × 0.50 m, divided into six approximately equal panels, each of them painted in a

different shade of grey. The shades of grey were obtained by mixing black and white matte

emulsion paint with different ratios. Prior to painting the board, thirty different black and white

combinations were painted on a test board. The reflectance of each combination was measured

at the 1550 nm and 808 nm wavelengths using an ASD field Spectroradiometer with a contact

probe, then six combinations were chosen and used to paint the multi-step board. The

combinations were selected so that they would cover a reflectance range between 10% and

70%. Afterwards, five ASD measurements were taken for each of the six panels of the multi-

step board, and the reflectance of each panel at each wavelength equalled the average of the

measurements, as shown in Table 3-7.

45

Table 3-7. Actual reflectance of each of six panels in the multi-step painted board.

Wavelength Panel 1 Panel 2 Panel 3 Panel 4 Panel 5 Panel 6

1550 nm 12% 23.8% 36.5% 48.4% 57.7% 64.8%

808 nm 14.6% 28.9% 42.9% 55.1% 64.3% 70.7%

The multi-step painted board was scanned indoors by each scanner separately at a random range

of 7.32 m. The board was also scanned outdoors at 4.4 m, 6 m and 8.6 m. The calibration models

were used to calibrate the intensity values of each panel in the multi-step board for the range

effect and to apparent reflectance. The estimated reflectance was compared to the panels’ actual

reflectance for accuracy evaluation.

3.5 The ability of NDI to minimize the incidence angle effects

To investigate the ability of the NDI of the 808 nm and 1550 nm to minimize the incidence

angle effects, an experiment was conducted in a laboratory setting using eighteen leaf samples

from six different tree species, three samples from each species. The leaf samples included:

grey alder (Alnus incana), common lime (Tilia x europaea), common alder (Alnus glutinosa),

hornbeam (Carpinus betulus), poplar (Populus sp.) and cherry (Prunus avium). The leaf

samples were collected from Peel Park in Salford, Manchester, UK. The experiment was

conducted using the P20 and the P40a instruments. There was no need to repeat the experiments

using the P40b and the P50 instruments, as the incidence angle effect is a factor of the

wavelength and the scanned target surface characteristics, and not of the instrument itself.

The leaf samples were divided into three groups, each group containing six leaf samples, one

sample from each species. Each group of leaves was suspended in a wooden frame by thin black

threads. The frame (Figure 3-5) was positioned at a fixed scan range of 6.5 m. A clear space of

2 m was ensured behind the frame to avoid any influence of the energy reflected from the rear

wall on the leaf sample’s backscattered intensity. The whole frame was rotated between scans,

increasing the incidence angle from 0 to 60 degrees, with scans conducted at 20 degree

intervals. A total of 24 scans were conducted. The intensity values of each leaf, at each

incidence angle, for each scanner, were extracted. The points near the edges of the leaves were

manually removed as they corresponded to partial hits. The intensity values were calibrated

into apparent reflectance using the calibration models described in Section 3.4. NDI was

calculated at each incidence angle for each leaf.

46

Figure 3-5. The wooden frame used in the leaf incidence angle experiment.

3.6 The ability of NDI to minimize the leaf structure effects

A leaf has a complicated internal structure, which significantly affects the interaction of

radiation with foliage (Jacquemoud and Baret, 1990). The ability of NDI to minimize the leaf

internal structure effects is a key parameter in successfully using NDI to estimate EWT at

canopy level, especially in forest environments, as the leaf internal structure varies between

different species and also within each individual species (Lichtenthaler et al., 1981).

Figure 3-6 shows an example of the leaf internal structure. The mesophyll layers are where the

photosynthesis take place. The palisade mesophyll cells are packed together and are responsible

for the majority of the photosynthesis, while the spongy mesophyll cells contribute less to the

photosynthesis and have gaps between them, filled with air or water. The number and thickness

of the mesophyll layers, in addition to the cellular arrangement within them, varies between

individual leaves within each species and also between different species (Jacquemoud and

Baret, 1990). If the leaf is fully dry, that is, it has zero water content, the weight of the leaf can

be considered the leaf dry weight, which when divided by the leaf surface area results in the

LMA (Poorter et al., 2009) (Equation (3.3)). The leaf mesophyll structure and LMA, in addition

to EWT, are the major elements affecting the interaction of NIR and SWIR wavelengths with

foliage (Ceccato et al., 2001; Gaulton et al., 2013). Thus, these three elements were the focus

of this research, while the remaining leaf structure components were ignored. To investigate

the effects of the aforementioned elements on the 808 and 1550 nm wavelengths, and on the

NDI, PROSPECT simulations were conducted.

47

Figure 3-6. Leaf internal structure and cellular arrangements.

𝐿𝑀𝐴 (𝑔 𝑐𝑚−2) =𝐿𝑒𝑎𝑓 𝐷𝑊

𝐿𝑒𝑎𝑓 𝑆𝐴 (3.3)

3.6.1 PROSPECT model

PROSPECT (Jacquemoud and Baret, 1990) is a radiative transfer model capable of simulating

the optical properties of plant leaves over the visible, near infrared and shortwave infrared

regions of the electromagnetic spectrum (400 nm to 2500 nm). The version used in this research

was PROSPECT-5 (Feret et al., 2008), which modelled the leaf optical properties using six

parameters: leaf structure coefficient (N), chlorophyll a and b content (Cab), carotenoid content

(Car), brown pigment content (Cb), leaf water content (Cw), and dry matter content (Cm). The

values of Cab, Car and Cb were kept constant at the model defaults, 47.7 µg cm-2, 4.4 µg cm-2

and 0 respectively, as they have minor effects on the NIR and SWIR wavelengths (Ceccato et

al., 2001; Gaulton et al., 2013). Zarco-Tejada et al. (2003) also used generic, fixed values for

the aforementioned variables while conducting PROSPECT and SAIL simulations to determine

the suitability of MODIS data to estimate canopy EWT at the landscape level. Cw will be

referred to as EWT in the upcoming sections. Cm represents the leaf dry matter content and is

quantified in the model as LMA (Feret et al., 2008). Cm will be referred to as LMA hereafter.

3.6.2 The effects of leaf structure coefficient on the NDI

N is the leaf mesophyll structure coefficient and is related to number and thickness of the

mesophyll cell layers and the cellular arrangement within them (Figure 3-6), covering a range

of values between 1 and 3 (Jacquemoud and Baret, 1990). To study how N affected the NDI,

the values of EWT and LMA were kept constant, each at an average value of 0.01 g cm-2, while

48

N was incrementally increased from 1.5 to 2.5, with an interval of 0.1. N values < 1.5

correspond to monocot leaves, which were outside the interest of this research, while N

values > 2.5 indicate that leaves are senescent (Jacquemoud and Baret, 1990). The NDI was

calculated for each N value.

3.6.3 The effects of LMA on the NDI

N and EWT values were kept constant at 2 (dimensionless) and 0.01 g cm-2 respectively. LMA

values covered the range between 0.0017 and 0.0157 g cm-2, based on the minimum and

maximum values reported in the LOPEX93 dataset (Feret et al., 2008), with an interval of

0.001 g cm-2. The NDI was calculated for each LMA value.

3.6.4 The effects of leaf structure coefficient on the NDI-EWT relationship

A total number of 111 EWT values, ranging between 0.0046 and 0.0162 g cm-2, which resulted

from actual leaf sample EWT measurements conducted in this research and fully described in

Chapters 4 and 5, were used. LMA was kept constant at 0.01 g cm-2, and for each EWT value,

N was changed between 1.5 and 2.5, with an interval of 0.1, resulting in 1221 combinations of

N and EWT. The NDI was calculated for each combination.

3.6.5 The effects of LMA on the NDI-EWT relationship

The same EWT dataset was used as described in Section 3.6.4. For each EWT value, LMA

changed between 0.0017 and 0.0157 g cm-2, with an interval of 0.001 g cm-2. N was kept

constant at 2 in all simulations. The NDI was calculated for each of the 1665 combinations of

LMA and EWT.

3.7 Results and discussion

3.7.1 TLS intensity data calibration

The intensity-range relationship for the four instruments deviated from the 1/R2 component of

the laser equation (Figure 3-7). The intensity-range relationships also varied between the

instruments, despite being built by the same manufacturer. Additionally, there was also a clear

difference between the P40a and the P40b instruments, although they were of the same model.

All instruments were found to be equipped with a near-distance intensity reducer to protect the

optics, which reduced the intensity for ranges less than 4 m (5 m for the P50 instrument). A

drop was seen in the intensity values up to around 7.5 m range for the P40a and the P50

instruments, and 9 m for the P20 and the P40b instruments, after which the intensity values

gradually increased.

49

Figure 3-7. The intensity-range relationships for the four instruments used in this study.

The P20 and the P40b instruments then showed similar behaviour as the intensity values

continued to gradually increase, before tending to level out after 15 m for the P20 and 20 m for

the P40b. For the P40a and the P50 instruments, the intensity values increased more

significantly, before starting to experience another drop after 12 m and 15 m respectively. The

intensity values then tended to level out for the P40a instrument after 18 m range, with a slight

increase at 20 m range. The intensity-range relationship was not investigated for the P50

instrument beyond 22 m as it was not needed for the data collection campaign carried out with

the instrument. For the remaining instruments, the intensity values remained constant until the

end range of the experiment (36 m). The intensity levelling out suggested that the instruments

were equipped with on-board range calibration adjustments that attempted to calibrate for the

range effect after 15 m for the P20, 18 m for the P40a and 20 m for the P40b. Thus, these ranges

were chosen for Iref in the range calibration model (Table 3-5). A similar observation was

reported for the Faro Focus3D 120 scanner which internally calibrated for the range effect after

15 m (Tan et al., 2016).

For all of the instruments, it was not possible to fit a single polynomial function to the intensity-

range relationship. Thus, two polynomial functions were used for each instrument. The split

point for the polynomial function was selected for each instrument to achieve a smooth

transition between the two functions. The degrees of the polynomial functions were chosen so

that the highest possible fitting accuracy could be achieved. Table 3-8 shows the degrees of the

polynomial functions for each instrument. It is worth mentioning that for the P20 and the P40a

instruments, polynomial functions were fitted only up to 15 m and 18 m ranges respectively, as

the instruments were to be used in indoor experiments and outdoors to scan willow plots, both

50

involving scanning trees at near ranges, as described in Chapter 4 and Chapter 6 respectively.

The P20 instrument was recalibrated and new polynomial functions were fitted up to 36 m range

to use the instrument in the forest data collection campaign (Chapter 5). As shown in Table 3-8,

a 3rd degree polynomial function was fitted between 2 and 5 m for the P40b instrument, unlike

the other instruments, for which a 2nd degree polynomial function was sufficient. This was done

to achieve a smooth transition between the two fitted polynomial function at the split point, and

this could not be achieved for the P40b instrument when a 2nd degree function was fitted at near

range. This difference between the instruments may be a result of them being equipped with

different near-distance intensity reducers. Figure 3-8 shows the fitted polynomial functions for

the P40b.

Table 3-8. Properties of the polynomial functions for the calibration models.

Scanner Polynomial function Split point Fitting accuracy (R2)

P20 6th degree function between 2 and 15 m ---- 97%

P20* 2nd degree function between 2 and 5 m

6th degree function between 5 and 36 m 5 m

99%

99%

P40a 2nd degree function between 2 and 4 m


99%

98%

P40b 3rd degree function between 2 and 5 m


99%

97%

P50 2nd degree function between 2 and 5 m


99%

99%

*Recalibration for the forest data collection campaign.

Figure 3-8. The P40b fitted polynomial functions for near ranges (red, 3rd degree function) and

for remaining ranges (blue, 6th degree function) with 5 m range chosen as the split point.

51

The polynomial functions can be described as follows:

For the P20 instrument:

IP = 0.0000005983 × R6 – 0.0000167105 × R5 – 0.0000791961 × R4 +

0.0070609346 × R3 – 0.0854688734 × R2 + 0.3909688599 × R +

0.0336314008

(3.4)

For the P20 recalibration:

IP = -0.0014419055 × R2 + 0.0123301563 × R + 0.015072154, for R ≤ 5 m (3.5)

IP = 0.0000000005 × R6 – 0.0000000751 × R5 + 0.0000041012 × R4 –

0.0001139564 × R3 + 0.0016819488 × R2 – 0.0122517431 × R + 0.071853401,

for R > 5 m

(3.6)

For the P40a instrument:

IP = -0.0167555733 × R2 + 0.2475767475 × R – 0.12114732, for R ≤ 4 m (3.7)

IP = -0.0000033794 × R6 + 0.0002243391 × R5 – 0.0058804139 × R4 +

0.0766866501 × R3 – 0.5126008324 × R2 + 1.6048373581 × R –

1.2338424945, for R > 4 m

(3.8)

For the P40b instrument:

IP = 0.0004042365 × R3 – 0.006086872 × R2 + 0.029380775 × R –

0.0089245614, for R ≤ 5 m (3.9)

IP = -0.00000000012 × R6 + 0.0000000133 × R5 – 0.0000004981 × R4 +

0.0000045267 × R3 + 0.0001134455 × R2 – 0.0022367096 × R +

0.0443796256, for R > 5 m

(3.10)


IP = -0.0003583715 × R2 + 0.0048220578 × R + 0.01973043, for R ≤ 5 m (3.11)

IP = -0.000000023 × R6 + 0.0000018088 × R5 – 0.0000550102 × R4 +

0.0008050998 × R3 – 0.0057017687 × R2 + 0.0170650149 × R +

0.0204361395, for R > 5 m

(3.12)

Where IP is the intensity from the polynomial function at a range R.

Figure 3-9 shows the relationship between the intensity values and the reference panels’ true

reflectance for all instruments. In Figure 3-9a, which shows the relationships for the P40a and

the P20 instruments, the intensity values cover the range between 0 and 1, as they were

internally stretched by the instruments to enhance the visual appearance of the point clouds.

52

This intensity alteration was done automatically by the instruments and access to the original

recorded intensity values before the alteration was not possible. According to the manufacturer,

the intensity alteration was constant, and thus, the intensity-reflectance relationship can be used

to calibrate the intensity to reflectance in any scan conducted by the same instrument. The

intensity-reflectance relationships could only be described as non-linear, and a 2nd degree

polynomial function was fitted to each intensity-reflectance curve with R2 > 0.99.

Figure 3-9. The intensity-reflectance relationships of the instruments used in this study.

Figure 3-9b shows the intensity-reflectance relationships for the P40b, the P20 (recalibration),

and the P50 instruments, in which the intensity values were not altered by the instruments to

enhance the visual appearance. This was achieved by using an intensity map editor provided by

the manufacturer, Leica Geosystems, which reversed the intensity alteration and restored the

original recorded intensity values. The relationship between the intensity values and the panels’

true reflectance, for the three instruments, can be described as linear (R2 > 0.99). However,

there was a slight nonlinearity that could lead to overestimation of reflectance if a linear model

was used, especially for the reflectance region less than 50%, where most of leaf reflectance

was expected to be. An overestimation of reflectance can lead to a significant error in EWT

estimation. It was therefore preferable to fit 2nd degree polynomial functions to the intensity-

reflectance relationships (R2 > 0.99) to better account for the nonlinearity.

The polynomial functions that describes the intensity-reflectance relationships of the different

instruments are as follows:


I = -0.9877937688 × ρ2 + 1.8706814918 × ρ – 0.0175632763 (3.13)

53

For the P20 recalibration:

I = -0.0260691455 × ρ2 + 0.1067444694 × ρ + 0.0003190931 (3.14)

For the P40a instrument:

I = -0.4714314827 × ρ 2 + 1.3390038055 × ρ – 0.0092605544 (3.15)

For the P40b instrument:

I = -0.0225515506 × ρ 2 + 0.0997507219 × ρ – 0.0000788046 (3.16)


I = -0.010138882 × ρ 2 + 0.0816307988 × ρ + 0.0003966758 (3.17)

Where I refers to the intensity and ρ is the reflectance.

3.7.2 Validating the intensity calibration models

Table 3-9 shows the errors in the reflectance estimation for each validation experiment. For the

Spectralon panels experiments, the estimated reflectance at each range of the experiment was

compared to the panel’s true reflectance and the relative error (E %) was calculated following

Equation (3.1). The results revealed high errors at 2 m range, shown in Table 3-9 as max error(a),

and generally higher errors at ranges ≤ 4 m than at the remaining ranges. This can be a result of

the calibration models not being fully able to calibrate the intensity for the near-distance

intensity reducer effects. The average of the relative errors at all ranges was the lowest for the

P20, followed by the P40a and the P50, then the P40b, with all average errors being < 4%. When

ranges ≤ 4 m were excluded, as no leaf-scanning experiments were planned at such near ranges,

the max error dropped, shown in Table 3-9 as max error(b). RMSE was not used for direct

comparison between all validation experiments, because the datasets had different mean and

numerical scales as a result of using 90% and 20% reference targets for validation. However,

RMSE was higher for the P40a than for the P20 (90% Spectralon panel used in both

experiments). This can be a result of the high error in the reflectance estimation at 2 m range

(12.3%). When measurements ≤ 4 m were excluded, RMSE was found to be equal in the two

experiments. For the P20 recalibration, the P40b and the P50, for which a reference target with

a nominal 20% reflectance was used, RMSE was higher for the P40b than the P50 and the P20

and also dropped when ranges ≤ 4 m were excluded.

54

Table 3-9. Errors in the reflectance estimation in the validation experiments. For the Spectralon

panels, Max error(a), average error(a) and RMSE(a) correspond to all ranges, while max error(b),

average error(b) and RMSE(b) correspond to ranges > 4 m. For the painted board, max error(a),

average error(a) and RMSE(a) correspond to all panels, while max error(b), average error(b) and

RMSE(b) correspond to panels with reflectance < 60%.

Scanner Validation

experiment

Min

error

(%)

Max

error(a)

(%)

Average

error(a)

(%)

RMSE(a)

Max

error(b)

(%)

Average

error(b)

(%)

RMSE(b)

P20 90% panel 0.6 1.5 1.1 0.011 1.5 1.2 0.011

P20*

20% panel

Painted

board

0

1.1

7.4

8.3

1.6

5.1

0.006

0.043

1.7

5.3

1

3.6

0.002

0.020

P40a 90% panel 0.3 12.3 1.7 0.025 1.7 1.2 0.011

P40b

20% panel

Painted

board

1.7

1.5

8.3

7.2

3.4

3.3

0.009

0.022

4.6

5

2.9

3.1

0.007

0.012

P50 20% panel 0.1 6.9 1.8 0.005 3.2 1.1 0.003

*Additional validation for the forest data collection campaign.

For the additional validation experiments conducted for the P20 and P40b instruments using the

multi-step painted board, the average of the relative errors was 3.3% and 5.1% for the P40b and

the P20 instruments respectively. For the P40b, the error was consistent in the two different

validation experiments, which suggested that the wavelength of the instrument was not affected

by the change in the target surface properties between the Spectralon panel and the wooden

painted board. However, for the P20, the error in the multi-step painted board validation

experiment was higher than that in the Spectralon panel experiment, suggesting that the NIR

wavelength utilized in the instrument was affected by the target surface characteristics. The

errors were higher in the two panels with reflectance > 60% than the remaining four panels.

When these panels were excluded, as reflectance > 60% was highly unlikely to be encountered

when scanning foliage, the errors dropped to 3.1% and 3.6% for the P40b and the P20

respectively. RMSE also dropped from 0.022 to 0.012 for the P40b and from 0.043 to 0.02 for

the P20.

The average of the relative errors observed in all the validation experiments was 2.6%, which

dropped to 2% when ranges < 4 m and multi-step painted board panels with reflectance > 60%

were excluded. Thus, the accuracy of the calibration was considered suitable for the purpose of

this study. It is worth mentioning that the intensity correction models were developed using

almost perfect Lambertian panels, while in reality leaf and canopy reflectance is bidirectional

and can be described by the Bidirectional Reflectance Distribution Function (BRDF). Leaf

55

BRDF not only depends on leaf surface spectral characteristics, but also on the direction of the

incident light and the direction of scattered light. This means that the laser beam incidence angle

and the TLS instrument viewing angle affect the amount of reflected energy that will reach the

sensor and then be recorded as backscattered intensity. Similar to the incidence angle effects,

accounting for leaf BRDF effects will be mandatory when using a single wavelength to estimate

EWT, as leaves in different parts of the canopy will have different reflectance, not only because

they have different EWT, but also because of their orientation in space in relation to the sensor.

However, the use of NDI can minimize such effects, similar to the use of vegetation indices to

minimize the effects of sun position and sensor viewing angle on satellite estimation of EWT.

Radiative transfer models also approximate leaves as Lambertian surfaces, considering that

combining multiple spectral bands can minimize leaf BRDF effects. Based on this assumption,

models such as PROSPECT, PROSAIL, and GeoSAIL have been successfully utilized in

estimating EWT from optical remote sensing data, as discussed in Section 2.3.2.

3.7.3 The ability of NDI to minimize the incidence angle effect

The effect of the incidence angle on both wavelengths was large in all species sampled (Figure

3-10), as the change in incidence angle between zero and 60 degrees reduced the SWIR

reflectance by an average of 47% and the NIR reflectance by an average of 52%. The change

in reflectance varied between species, with minimum and maximum changes being 29%

(hornbeam) and 67% (poplar) respectively in SWIR reflectance, and 36% (hornbeam) and 68%

(poplar) respectively in NIR reflectance. The effect of incidence angle was higher on NIR

wavelength than in SWIR, agreeing to the observations reported in Hancock et al. (2017). Using

NDI largely minimized the incidence angle effects for all six species (Figure 3-10c). Changing

the incidence angle between zero and 40 degrees caused an average change in NDI of 6.7%

across all leaf samples, with the change in NDI ranging between 0.2% (hornbeam) and 12%

(grey alder). Changing the incidence angles between zero and 60 degrees caused an average

change of 13.7% in NDI, with the change in NDI varying between 3.7%, and up to 19% in the

grey alder leaf samples. One reason for the more significant change in NDI at 60 degrees

incidence angle can be that leaves are not perfectly Lambertian surfaces, meaning they only

follow the cosine law to an extent. Furthermore, at 60 degrees incidence angel it became very

difficult to accurately distinguish between points corresponding to leaf samples and those

corresponding to the edge of the frame, because of the partial hits between the leaves and the

frame. This has contributed to the deviation observed.

56

Overall, the average change in NDI for all species and incidence angles, including the

deviations at 60 degrees for the grey alder leaves, was 9%. For the same leaf samples, the

change in NDI caused by the change in EWT between minimum and maximum was 53%,

showing that EWT had more significant impact on NDI than the remaining effects of the

incidence angle. Hancock et al. (2017) reported similar observations for NDI of 1064 nm and

1545 nm, also observing deviations at 10, 40, and 60 degrees incidence angles for some leaf

samples, which was more than the deviation observed at 60 degrees in this study, and

concluding that the change in NDI caused by the change in EWT was more significant than that

caused by the incidence angle effects.

Figure 3-10. The reflectance-incidence angle relationship for leaf samples for the 1550 nm

wavelength: (A1) group 1, (A2) group 2 and (A3) group 3; the reflectance-incidence angle

relationship for the 808 nm wavelength: (B1) group 1, (B2) group 2 and (B3) group 3, and the

NDI-incidence angle relationship: (C1) group 1, (C2) group 2 and (C3) group 3.

3.7.4 The ability of NDI to minimize the leaf structure effects

Changing N affected the modelled leaf reflectance in the visible, NIR and SWIR regions of the

electromagnetic spectrum, with higher values of N leading to an increasing reflectance (Figure

3-11a). The two wavelengths in the scope of this study, 808 nm and 1550 nm, showed similar

sensitivity to the change in N (Figure 3-11b). Combining the two wavelengths in the NDI

57

minimized the effects of N. However, it did not entirely eliminate these effects (Figure 3-11b).

A leaf with a more compact mesophyll structure would have a slightly higher NDI value than

a leaf with a more differentiated structure, even if they both had an identical EWT.

LMA affected the leaf reflectance in the NIR and SWIR regions only, with the least effect being

around the water absorption wavelengths of 1400 nm, 2500 nm, and especially 1900 nm.

Increasing LMA resulted in a lower leaf reflectance (Figure 3-12a). The 808 nm and the

1550 nm wavelengths were both sensitive to the change in LMA, and combining them in the

NDI minimized, but did not eliminate, the effects (Figure 3-12b). A leaf with respectively lower

dry matter content would have a slightly lower NDI than a leaf with higher dry matter content

that has the same surface area and EWT value.

Figure 3-11. (a) Effects of N on the leaf reflectance in the visible, NIR and SWIR regions of

the electromagnetic spectrum, and (b) effects of N on 808 nm wavelength, 1550 nm wavelength

and NDI.

Figure 3-12. (a) Effects of LMA on the leaf reflectance in the visible, NIR and SWIR regions

of the electromagnetic spectrum and (b) effects of LMA on 808 nm wavelength, 1550 nm

wavelength and NDI.

58

The NDI – EWT relationship was affected by the change in N, with an increase in N leading to

a shift in the trendline of the NDI – EWT relationship downwards (Figure 3-13). The effects of

N appeared to be larger for higher EWT values. The relationship was also affected by the LMA

value, with an increase in LMA value causing the trendline of the NDI – EWT relationship to

be shifted up (Figure 3-14). Unlike N, the effects of LMA were slightly larger for lower EWT

values. It is worth mentioning that although N and LMA were considered uncorrelated

parameters in the simulations, for the sake of studying their effects on NDI individually, they

are highly correlated in reality. N is correlated to the Specific Leaf Area (SLA) parameter,

which is the leaf surface area divided by the leaf dry mass, and an increase in SLA leads to a

decrease in N (Jacquemoud and Baret, 1990). Thus, N is also highly correlated to LMA, as

LMA is the reciprocal of SLA. A thinner leaf would frequently have a lower N value than a

thicker leaf, and correspondingly a lower LMA value. Although the PROSPECT simulations

revealed that both N and LMA individually slightly affected NDI, when their effects were

combined they would be minimized as they would cancel each other out. Thus, a change in NDI

would be mainly caused by a change in EWT, with some minor influence of N and LMA. Thus,

the NDI has the potential to be used to estimate EWT at canopy level in forest plots with mixed

species, as the leaf internal structure would have a minimal effect on the estimation.

Figure 3-13. Effects of leaf structure coefficient on the NDI – EWT relationship.

59

Figure 3-14. Effects of LMA on the NDI – EWT relationship.

3.8 Summary

This chapter described the characteristics of the TLS instruments used in this study and the

method for combining their data to produce 3D EWT estimations. The details of the calibration

work for each instrument were given, then the ability of the NDI of 808 nm and 1550 nm

wavelengths to minimize the incidence angle and leaf internal structure effects was

investigated.

The intensity data from the four TLS instruments involved in this research was found to be

heavily affected by the range effect. None of the intensity – range relationships followed the

laser equation. In addition, each TLS instrument was found to have its unique intensity-range

relationship, thus, each instrument needed its own calibration model. The instruments were

found to be equipped with a near-distance intensity reducer. Plus, they also seemed to be

equipped with far-distance intensity amplifiers and on-board range calibration adjustments,

which attempted to calibrate the intensity for the range effect starting from a specific range,

which differed between the instruments. The calibration models developed were found to be

able to calibrate the intensity for the range effect and to apparent reflectance with low errors.

Although the errors observed at ranges < 4 m were higher than the remaining ranges, this was

not considered a problem as none of the scans planned in the data collection campaigns were to

be conducted at such near ranges.

The incidence angle had a severe influence on the intensity data of both the near and the

shortwave infrared wavelengths. Using NDI minimized the incidence angle effect with no need

for further radiometric calibration for a variety of leaf samples from different tree species. Some

deviation, however, was observed at 60 degrees incidence angle, which may introduce some

60

errors while applying this method at the canopy level in real forest environments, as laser beams

will hit the leaves at all possible incidence angles (Kaasalainen et al., 2018).

PROSPECT simulations revealed that the leaf internal structure, defined in the model as the

leaf mesophyll structure coefficient (N) and LMA, affected the reflectance at both the 808 nm

and the 1550 nm wavelengths. Increasing N resulted in a higher leaf reflectance while

increasing LMA reduced the leaf reflectance. The simulations also revealed that combining the

two wavelengths in the NDI can minimize, but not entirely normalize, such effects. EWT was

found to be the main parameter affecting NDI, but there remained some minor influences of N

and LMA, which may reduce the accuracy of EWT estimation in mixed-species sites that

exhibit significant variation in N and LMA, for instance, sites that combine green and senescent

leaves.

The experiments described in this chapter and the results obtained showed the possibility of

retrieving reflectance data from the four TLS instruments tested with low errors, and also

demonstrated the ability of NDI of the two wavelengths in the scope of this research to minimize

the incidence angle and leaf internal structure effects. Such ability can allow the use of NDI to

estimate EWT at the canopy level without the need for radiometric corrections for the incidence

angle effects, and for the variation in the leaf internal structure within each individual species

or between different species. This can allow the use of NDI to estimate EWT in 3D at canopy

level.

61

Chapter 4. EWT estimation in indoors dry-down experiment

4.1 Introduction

This chapter describes a dry-down experiment conducted in a laboratory setting using four

small trees from two different species. The instruments used in the experiment were the Leica

P20 and the P40a. The aim of the experiment was to investigate the ability of NDI to estimate

EWT at leaf level, and the possibility of upscaling the estimations to canopy level. Challenges

associated with the estimation of EWT at the canopy level were studied, which included the

accuracy of the registration, the possibility of calculating NDI on a point-by-point basis, and

the effect of the woody materials on the EWT estimation accuracy. The experiment also

examined the vertical distribution of EWT at the canopy level and the ability of the proposed

approach to detect the change in EWT over a short period of time. Additionally, the experiment

investigated how the drying pattern varied between the two different species. However, the

experiment can only be considered a proof-of-concept as it included one deciduous and one

conifer species, and 3D EWT point clouds were generated for a single tree from each species.

The experiment was a step towards transferring the EWT estimation approach to a real forest

environment. The content of this chapter was published in Elsherif et al. (2018).

4.2 Experimental setup

The dry-down experiment was conducted at the School of Environment and Life Sciences,

Salford University, Manchester, UK. The trees involved in the experiment were two deciduous

Snake-bark maple (Acer davidii), with an approximate height of 2.6 m each (Figure 4-1a), and

two Corsican pine (Pinus nigra) conifers, approximately 0.9 m in height (Figure 4-1b). The

duration of the experiment was eight days for the deciduous canopies and nine days for the

conifer canopies. For each species, one tree served as a control unit and was watered regularly,

whilst the other acted as a dry-down unit and was left to dry naturally.

The trees, together with a 50% Spectralon panel, were placed 6.5 m away from a tripod,

ensuring a clear space of at least 1 m between them and the wall behind. Neither the trees nor

tripod were moved during the entire period of the experiment, thereby ensuring the scanners

occupied the same survey point in all scans and faced the trees from the same viewing angle.

The canopies were scanned by the P20 instrument followed by the P40a instrument on each day

of the experiment, except for two days on which access to the building was not granted,

resulting in 6 scans for the deciduous canopies and 7 for the conifer canopies. For consistency,

62

all scans were conducted at 11 am, with a duration of 10 minutes for each instrument, and leaf

sampling (Section 4.3) was carried out immediately after the scans to ensure that the leaf

samples represented canopy EWT at the time of the scan, as leaves lose moisture during the

day as a result of photosynthesis. No scans were conducted predawn, and all the scans were

carried out with the instruments’ highest point spacing (0.8 mm at 10 m).

Figure 4-1. The trees involved in the indoors dry-down experiment: (a) the deciduous canopies

and (b) the coniferous canopies, while (1) indicates the control units and (2) indicates the dry-

down units.

4.3 Leaf sampling and biochemistry measurements

4.3.1 Deciduous leaves

Three leaf samples were collected daily from the dry-down unit, except for days 4 and 7,

resulting in a total of 18 leaf samples. Collecting more daily samples was not possible in order

to avoid defoliation of the small canopy. To add species variety to the experiment, nine

additional leaf samples were collected randomly from five different species in Peel Park in

Salford, Manchester. The leaf samples included: three leaves of grey alder (Alnus incana), two

leaves of common lime (Tilia x europaea), one leaf of common alder (Alnus glutinosa), one

leaf of poplar (Populus sp.), and one leaf of cherry (Prunus avium). Processing the leaf samples

followed the steps described in Section 3.3.4. The fresh weight of each leaf was measured

immediately on collection, then the leaves were suspended in a wooden frame and successively

scanned by the P20 and P40a scanners, at a distance of 6.5 m. NDI was calculated following

Equation (2.5) after calibrating the intensity to apparent reflectance using the calibration models

described in Section 3.7.1. EWT of each leaf was calculated following Equation (2.1) after

measuring the surface area and dry weight of each leaf sample.

63

4.3.2 Conifer needles leaves

Three needle samples were collected daily from the dry-down unit, except for days 5 and 8.

Each sample consisted of 25 to 45 needles. The weight of the needles in the sample was

measured and considered the sample fresh weight. The needles in each sample were then taped

together, side by side from the top, to form a wider surface that could be scanned. They were

scanned as a single unit by both the P20 the P40a scanners. The surface area of each sample

was considered the summation of the surface areas of the needles in the sample, while the

surface area of each individual needle was estimated as the length of the needle multiplied by

the needle width (estimated at 1 mm). To measure the needle samples’ dry weight, the samples

were dried in an oven for eight days at 60 degrees Celsius. Samples were weighed every two

days until no change in weight was detected. EWT of each needle sample was calculated

according to Equation (2.1). Similar to the broadleaf samples, NDI of the reflectance was

calculated for each needle sample following Equation (2.5).

4.4 Canopy level data processing and analysis

4.4.1 Point cloud processing

Processing the canopy level point clouds to produce an NDI point cloud on each day of the

experiment followed the steps described in Sections 3.3.2 and 3.3.3. The scans were imported

into Leica Cyclone and the partial canopy hits were reduced from the point clouds using the

Cyclone partial hits filter on medium setting. Each P40a and P20 point cloud pair was registered

in Leica Cyclone. The P20 point clouds were then filtered to match the same number of points

in the corresponding P40a point clouds using a nearest neighbour function, as described in

Section 3.3.3.

The registered point clouds were then calibrated for the range effect on a point-by-point basis

using the calibration models described in Section 3.7.1. NDI was calculated for each pair of

P40a/P20 scans on a point-by-point basis and an NDI point cloud for each day of the experiment

was generated. Mean NDI at canopy level was compared to the mean EWT of the leaf samples

of the dry-down unit on each day of the experiment. The NDI point clouds were then

transformed to EWT point clouds by applying Equation (4.1) and Equation (4.2), on a point-

by-point basis, for the deciduous and conifer canopies respectively, derived in Section 4.5.1.

4.4.2 Removing the woody materials

The histogram of the EWT distribution at canopy level on day 1 was used to separate the woody

materials from the leaves by choosing a separation threshold by trial and error, until few points

64

corresponding to leaves were incorrectly filtered as woody materials. The threshold was then

applied to the point clouds on all days of the experiment. In the case of the conifer canopies,

the threshold incorrectly extracted too many points corresponding to needles in the point cloud

of day 9, as they represented very dry needles that were misclassified as wood. Thus, the

identified woody points for day 1 were used as a reference, and were compared to the filtered

points on day 9. The cloud-to-cloud distance module in CloudCompare v.2.6.2 software

package was used to identify the filtered points on day 9 that were less than 1 mm away from

the corresponding filtered points on day 1. These points corresponded to the stem and branches.

The remaining points were extracted and re-added to the needle point cloud.

4.4.3 Studying the effect of wood on the EWT estimations

The average estimated EWT for each day of the experiment was calculated from the point cloud

of the dry-down unit, as the summation of the water content of all points in the EWT point

cloud, divided by the number of points. The estimated EWT at canopy level, before and after

filtering the woody materials, was compared to EWT from leaf samples on each day of the

experiment, not with the aim of validating the estimation, which requires destructively sampling

the whole canopy, or collecting a different set of leaf samples for the sole purpose of validating

the EWT estimates, but to study the effect of the presence of woody materials on upscaling

EWT estimation from leaf to canopy level.

4.4.4 Detecting the change in EWT

The estimated EWT of the control and dry-down units, on each day of the experiment, after

removing the woody materials, was plotted against the corresponding day to investigate the

ability of the proposed approach to detect the change in EWT.

4.4.5 Studying the EWT vertical profiles

To study the vertical heterogeneity of the EWT, after filtering the woody materials, the EWT

point clouds of the dry-down unit on all days of the experiment were divided into 9 horizontal

layers (5 horizontal layers for the conifer canopy) and the average EWT for each layer was

calculated and plotted against the corresponding height.


4.5.1 Leaf level results

A strong linear relationship between NDI and EWT was found for the Snake-bark maple leaf

samples (Figure 4-2a, R2 = 0.82, P < 0.05). The following equation was derived from the

relationship, which can be used to estimate EWT from NDI for this species:

65

EWT (g cm-2) = 0.0594 × NDI – 0.0052 (4.1)

A strong linear relationship was also found between NDI and EWT for the additional nine leaf

samples (Figure 4-2b, R2 = 0.94, P < 0.05). The strong relationship holds (R2 = 0.91, P < 0.05)

when all the leaf samples were combined together (Figure 4-2c).

Figure 4-2. Leaf level results of NDI against EWT for (a) Snake-bark maple leaf samples, (b)

the additional leaf samples and (c) all leaf samples combined.

For the conifer needles, NDI was found to be highly correlated to EWT (Figure 4-3, R2 = 0.74,

P < 0.05). The relationship between NDI and EWT for this species had significantly different

slope and intercept than that of the deciduous species, as a result of the different leaf internal

structure between broadleaf and needles. The NDI – EWT relationship can be described as:

EWT (g cm-2) = 0.1148 × NDI – 0.0022 (4.2)

Figure 4-3. Leaf level results of NDI against EWT for the conifer samples.

4.5.2 Canopy level EWT estimation

The registration accuracy, reported by Leica Cyclone, of the P40a/P20 point clouds obtained on

all days of the experiment was very similar, as the scan geometry was constant. RMSE of the

registration ranged between 0.7 and 0.8 mm for all the scans, which was very close to the

66

utilised scan resolution (0.8 mm at 10 m). Figure 4-4 shows a 3D EWT point cloud for the

control and dry-down units of the deciduous canopies on day 8, alongside the histogram of the

water content distribution. Figure 4-5 shows the same for the conifer canopies on day 9. Visual

inspection of the EWT point clouds and histogram on all days revealed that the woody materials

had higher water content than the leaves in the deciduous canopies, possibly because the trees

were young and the woody materials were green. Green wood can have similar moisture content

to leaves or even higher (Waterman et al., 1983). In the case of the conifer canopies, the woody

materials (brown and mature) had lower water content than the needles. The visual inspection

also showed some stress in the bottom part of the dry-down canopies in comparison to the

control canopies.

Figure 4-4. For the deciduous canopies, (a) 3D EWT (g/cm2) distribution of the control unit

(left) and the dry-down unit (right) on day 8 and (b) the histogram of the EWT (g/cm2)

distribution for the dry-down and control units combined.

Figure 4-5. For the conifer canopies, (a) 3D EWT (g/cm2) distribution of the control unit (left)

and the dry-down unit (right) on day 9 and (b) the histogram of the EWT (g/cm2) distribution

for the dry-down and control units combined.

(a) (b)

(a) (b)

67

Thresholds of 0.028 g/cm2 for the deciduous trees was applied to the EWT point clouds to

extract the woody materials, which had EWT > 0.028 g/cm2. For the conifers, the woody

materials were found to have EWT < 0.011 g/cm2 and the threshold was used for leaf/wood

separation. Figure 4-6 illustrates the extracted woody materials of the EWT point clouds shown

in Figure 4-4 and Figure 4-5.

Figure 4-6. The extracted woody materials, (a) the deciduous canopies on day 8 and (b) the

conifer canopies on day 9, many needles were incorrectly filtered as wood in the conifer dry-

down unit.

NDI at canopy level for the dry-down units, before and after extracting the woody materials,

were plotted against EWT of leaf samples (Figure 4-7). The results revealed a linear relationship

with R2 of 0.57 and 0.48 for the deciduous and conifer canopies respectively before filtering

the woody materials (P > 0.05). Filtering the woody materials had little effect on the NDI –

EWT relationship for the deciduous canopy, but improved the relationship for the conifer

canopy (R2 = 0.73, P < 0.05). In addition, the NDI – EWT relationship for the deciduous canopy

was affected by the measured EWT of the leaf samples on day 6 being higher than the previous

days (see Figure 4-8), but would be expected to be lower as the canopy had dried more.

Excluding the results of day 6, likely to result from a laboratory measurement error, improved

the correlation (R2 = 0.89, P < 0.05).

(a) (b)

68

Figure 4-7. NDI at canopy level against EWT of leaf samples for the dry-down units, (a) the

deciduous canopies and (b) the conifer canopies. The outlier on day 6 is included.

4.5.3 Studying the effect of wood on the EWT estimations

The estimated EWT at canopy level for the dry-down units were compared to EWT of leaf

samples (Figure 4-8) and the relative error was calculated. The results revealed an

overestimation on all days for the deciduous canopy when woody material was included, except

for day 6, with an average of relative errors being 4.9%, and significant underestimation for the

conifer canopy, with an average of relative errors equalling 16.6%. Studying the errors,

alongside the histograms and point clouds of the EWT distribution, revealed that the higher

error in EWT estimation in the conifer canopy seemed to be a result of the canopy having

mature wood and a higher wood to leaf ratio than the deciduous canopy.

Figure 4-8. Estimated EWT at canopy level with and without the woody materials against EWT

of leaf samples for the dry-down unit, (a) deciduous and (b) conifer.

Filtering of the woody materials reduced the average error in EWT estimation to 2.9% for the

deciduous canopy, and significantly decreased the average error in the estimation to 2.6% for

the conifer canopy. This revealed the strong effect of the presence of the woody materials on

the upscaling of the TLS estimation of EWT from leaf to canopy level, especially when the

wood was brown and mature.

(a) (b)

(a) (b)

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4.5.4 Detecting the change in EWT

Figure 4-9 shows how the estimated EWT from the TLS measurements changed between the

first and last day of the experiment for the control and the dry-down deciduous canopies, while

Figure 4-10 shows the same for the conifer canopies. For the deciduous canopies, the control

unit showed an almost constant EWT, with a slight increase in the first two days then a slight

decrease in the remaining days. The dry-down unit, on the other hand, lost EWT throughout the

days of the experiment, with EWT on the last day of the experiment being 15% less than that

on the first day. The change in EWT appeared to be more significant in the first two days, as

the canopy lost 7.3% of EWT, while it lost a total of 7.9% EWT between the third and last day

of the experiment. In addition, a logarithmic model with R2 > 0.99 can describe the drying

pattern of the tree and could possibly be used to predict EWT beyond the duration of the

experiment.

Figure 4-9. The change in the estimated EWT from TLS measurements over the duration of the

experiment for the deciduous canopies.

Figure 4-10. The change in the estimated EWT from TLS measurements over the duration of

the experiment for the conifer canopies.

70

Furthermore, TLS could detect the daily change in EWT for the deciduous tree, showing that

the tree lost approximately 3.7% EWT per day in the first two days in the experiment, and 1.6%

EWT per day in the remaining five days. The ability of TLS to detect such fine changes in EWT

suggested that there could be a potential in using TLS to detect canopy EWT changes

throughout the day, and compare predawn EWT to midday EWT to quantify how much

moisture plants lose during photosynthesis.

For the conifer canopies, a 6% increase in EWT occurred between day 1 and day 4 for the

control unit, before EWT tended to be almost constant throughout the remaining days of the

experiment. This could be a result of the tree receiving more daily water than it was getting in

the nursery and/or the tree already being stressed before the start of the experiment. For the dry-

down unit, the overall trend shows a slight decrease in EWT throughout the days of the

experiment, with EWT on day 9 being 3.4% less than EWT on day 1. This was expected as

conifer canopies are drought tolerant and use water efficiently (Moran et al., 2017), suggesting

that nine days were not enough time to cause severe stress.

4.5.5 Studying the EWT vertical profiles

Figure 4-11 compares the vertical distribution of EWT for the dry-down units on the first and

last day of the experiment. The results revealed that the canopies had higher water content in

the upper canopy than the lower canopy. For the deciduous canopy, the bottom of the canopy

had 35% lower water content than the top on day 1, and 39.7% lower water content on day 8.

For the conifer canopy, the bottom of the canopy had 33% lower water content than the top on

day 1, and 40% lower water content on day 9.

Figure 4-11. The vertical distribution of EWT for the dry-down units, (a) deciduous and (b)

conifer, after filtering the woody materials.

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Comparing the EWT vertical profile on day 1 and on the last day revealed that the deciduous

canopy lost moisture from all parts of the canopy relatively evenly as it dried, with slightly

greater water loss from the lower canopy. For conifer canopy, upper canopy EWT remained

almost constant over the course of the experiment, with slightly greater water loss from the

lower canopy. Although the deciduous and coniferous canopies were not fully destructively

sampled to validate the EWT vertical profiles, the observations obtained agreed with the

findings reported in Chavana‐Bryant et al. (2016), a study in which younger leaves, typically

in canopy top layers, were reported to have higher EWT than older leaves for 1099 leaf samples

collected from 12 lowland Amazonian trees. Zhu et al. (2017) also reported EWT vertical

heterogeneity in 20 small plants from five different species: dwarf schefflera, weeping fig,

Chinese banyan, ficus, and Zanzibar gem, highlighting that EWT was always higher in canopy

top than in canopy bottom. This was also explained as a result of new leaves at the top of the

canopy having higher water content than older leaves (Mooney et al., 1977). The EWT vertical

heterogeneity and its possible causes are further investigated in Section 5.5.4.

4.6 Summary

This chapter described an indoor dry-down experiment conducted using two deciduous snake-

bark maple canopies and two Corsican pine conifer canopies. The experiment aimed at

investigating the relationship between the NDI of the 808 nm and the 1550 nm wavelengths

and the EWT at leaf level and the possibility of using NDI to produce 3D EWT estimates at

canopy level.

NDI was found to be highly linearly related to broadleaf EWT across various tree species, and

also to needle EWT of the Corsican pine conifer canopy. However, as the experiment included

samples from a single conifer species, the results obtained can only be considered preliminary

and further experiments that include various conifer species are necessary in order to determine

whether the NDI-EWT relationship will hold or not.

At the canopy level, a high registration accuracy was obtained for each P40a/P20 point cloud

pair on each day of the experiment, because of the similarity in the scan geometry. NDI

estimated EWT on a point-by-point basis for the deciduous and conifer canopies, generating

3D distributions of EWT that revealed some vertical heterogeneity. The young leaves and

needles in the upper canopy had higher water content than the older leaves/needles in the lower

canopy. This, if also observed in a forest environment, can affect passive optical spaceborne or

airborne sensor estimation of EWT, as measurements from such instruments will be dominated

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by the canopy top (Liu et al., 2015) (see Section 5.5.4 for EWT vertical profiles in forest

canopies).

NDI overestimated the EWT for the deciduous canopy, and underestimated it for the conifer

canopy on all days of the experiment, with the errors in the EWT estimation being proportional

to the amount of woody material in the point cloud. Filtering the woody material significantly

improved the EWT estimation accuracy for the conifer canopy, while having a minimum effect

for the deciduous canopy, most likely because the wood was young and green in the deciduous

canopy, but was brown and mature in the conifer canopy.

The 3D EWT estimates showed that the two different species had different drying patterns. The

deciduous tree seemed to be equally losing EWT from all layers in the canopy while the tree

dried between the first and last day of the experiment. On the other hand, the conifer canopy

maintained an almost constant EWT in the upper canopy layers while losing water from the

bottom layers. It was also possible to detect the change in EWT between the days of the

experiments, with the change being more obvious in the deciduous canopy than the conifer

canopy.

This experiment showed the potential of dual-wavelength TLS methods to better characterise

and study heterogeneity in biochemistry within tree canopies, which can provide a new insight

into tree stress and physiology. However, experiments in real forest environments need to be

conducted to assess the applicability of the proposed EWT estimation approach in larger

canopies and outdoor conditions (for example in the presence of wind). This was investigated

in fieldwork campaigns, described in Chapters 5 and 6.

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Chapter 5. Mapping of forest canopy EWT in three dimensions

5.1 Introduction

Forests are of great importance for humankind and the environment because of the essential

ecological, economic and social services they provide (Yao et al., 2014). They play a major

role in the global carbon and hydrological cycles (Pan et al., 2011), and influence the climate

as a result of exchanging water, energy, carbon dioxide, and other chemicals with the

atmosphere (Bonan, 2008). However, natural and anthropogenic threats, such as climate

change, drought, disease infections, pest infestations, wildfires, land use change and

deforestation, threaten forest health (Lewis et al., 2015; Millar and Stephenson, 2015). Forest

health monitoring is critical to understand how forests react to such stressors (Ferretti, 1997;

Trumbore et al., 2015), and also for early detection of drought stress, symptoms of disease, and

risk of wildfire (Meentemeyer et al., 2008).

As discussed in Section 2.3, optical RS data, airborne and spaceborne, have been widely

adopted in estimating canopy EWT as a vegetation stress indicator to overcome the limitations

of in-situ approaches (destructive methods and field spectroscopy), which are time and effort

consuming and impractical for large areas (Pu et al., 2003; Dash et al., 2017). However, the

effects of EWT vertical heterogeneity on the optical RS estimation of EWT still needs to be

investigated. The results obtained in Chapter 4 showed the potential of using dual-wavelength

TLS for estimating canopy EWT in 3D and generating EWT vertical profiles that can be used

to quantify and study the EWT vertical heterogeneity within a canopy. However, the

relationship between NDI and EWT at canopy level was investigated for small individual trees

in a controlled environment only. In this chapter, the method was transferred to a real forest

environment, and NDI was used to produce 3D estimations of EWT in a mixed-species

broadleaf deciduous forest plot in Wytham Woods, Oxford, UK. The aims of the data collection

campaign were to: (i) test the ability of NDI to generate 3D EWT estimates in a real forest

environment where partial canopy hits, wind effect, and variation of leaf internal structure

between species can affect the accuracy of the EWT estimates, and (ii) examine the vertical

variation of EWT within forest canopies. The content of this chapter was published in (Elsherif

et al., 2019d). Prof. Yadvinder Malhi and Dr Alexander Shenkin, Environmental Change

Institute, University of Oxford, have contributed to the results interpretation presented in

Sections 5.5.1 and 5.5.4.

74

5.2 Study area and TLS scanning setup

The data collection campaign took place in Wytham Woods near Wytham village

(51.78° N, 1.31° W) in Oxfordshire, UK, between 22nd and 31st of May 2017. Wytham Woods

is approximately 400 ha and consists of ancient woodland and naturally-regenerated secondary

woodland, in addition to nineteenth and twentieth-century plantations (Morecroft et al., 2008).

It has been owned by the University of Oxford since 1942 and is considered one of the most

important sites for ecological research in the world (Morecroft et al., 2001). The fieldwork data

were acquired in a 35 × 45 m rectangular plot around the treetop canopy walkway in the 18 ha

Wytham core plot (Figure 5-1 and Figure 5-2). Wytham core plot is a permanent sample plot,

established in the woodland for research purposes (McMahon et al., 2015).

The site was dominated by Quercus robur (oak) and Acer pseudoplatanus (sycamore) trees, in

addition to a number of Fagus sylvatica (beech) and Fraxinus excelsior (ash) trees. The

fieldwork campaign took place in non-windy, non-rainy conditions at an average temperature

of 21° Celsius. Thirteen trees around the canopy walkway were selected for sampling, based on

how accessible their leaves were from the canopy walkway (Figure 5-2). Ten scanning positions

were set around the walkway in locations corresponding to low density canopy cover to reduce

the effect of occlusion by lower branches and obtain as much detail (laser beam returns) as

possible from the thirteen sampled trees (Figure 5-2).

Figure 5-1. The study area: (a) Wytham woods and the location of Wytham core plot and (b)

the treetop canopy walkway.

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Figure 5-2. The 35 × 45 m rectangular plot and the thirteen sampled trees (indicated by numbers

assigned during fieldwork). Black indicates trees that were not sampled.

At each scan position, full-hemisphere scans (360° × 270°) were conducted by the P40b and the

P20 instruments, mounted consecutively on the same tripod, with a resolution (point spacing)

of 3 mm at 10 m. Four Leica black and white registration targets were used to link each pair of

consecutive scanning positions. The scans were conducted over a period of two days. On the

first day, TLS data were collected from scanning positions S1 to S6, with the duration of each

scan being approximately fifteen minutes for each instrument. Scanning began at 10 AM and

the total scanning duration, including the time needed to move the instruments between

different scanning positions, was approximately two hours. This was followed by leaf sampling,

for the purpose of validating the EWT estimation (Section 5.3.2), from trees number 1, 2, 3, 4,

5, 6, and 8. No samples for validation were collected from the ash tree, labelled 7, as it was the

only ash tree accessible from the treetop canopy walkway, and samples for building the EWT

estimation model were collected from it (Section 5.3.1). It was acknowledged that canopy EWT

may have undergone some changes over the two hours of scanning, as trees lost moisture during

photosynthesis, and that the ideal leaf sampling approach was to collect the samples at the same

time of scanning, ensuring that they accurately represented the canopy EWT at the time.

However, this was not possible due to lack of personnel. On the second day, scans were carried

out from scanning positions S7 to S10, starting at 10 AM and lasting for approximately

90 minutes, followed by collecting leaf samples for validation from trees number 9, 10, 11, 12,

and 13. Afterwards, leaf samples for building the EWT estimation models were collected and

processed as described in Section 5.3.1.

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5.3 Leaf sampling and biochemistry measurements

5.3.1 Samples for building the EWT estimation model

Eighty-four leaf samples were collected randomly from various trees in the plot. The priority

in sampling leaves for the EWT estimation model was to ensure a representative and broad

sample of species, leaf types and EWT. Samples were collected from low branches in the

canopy bottom that were accessible from ground using a tree pruner, and also from the canopy

top, which was accessible using the treetop canopy walkway. The canopy top leaf samples

predominantly represented sun leaves, while the canopy bottom leaf samples predominantly

represented shade leaves. Sun leaves grow in the well-lit regions of the canopy and are usually

thicker and have higher photosynthetic rates than shade leaves (Lichtenthaler et al., 1981;

Terashima et al., 2005). The leaf samples included: 18 oak leaf samples, 22 beech leaf samples,

20 sycamore leaf samples, and 24 ash leaf samples. The ash leaf samples were individual

leaflets of the compound leaves. Leaf sampling was carried out for each species separately.

That is, the oak leaf samples were collected first, and the fresh weight of each sample was

measured in field, immediately on collection, using an electronic balance (one milligram

precision). The samples were then suspended in a wooden frame, positioned 8.6 m away from

a tripod, and were scanned in field using the P40b, followed by the P20 instrument, with a

resolution (point spacing) of 0.8 mm at 10 m. The time gap between collecting the samples and

scanning them was less than fifteen minutes, and the duration of the scan was approximately

five minutes for each instrument. Afterwards, the same sampling approach was repeated for

each species, one after another.

The leaf samples were then transferred to the laboratory and processed as described in Section

3.3.4 to measure their surface area, dry weight, EWT, and NDI. It is worth mentioning that

before drying the samples in an oven, they were left to dry naturally over a period of two weeks.

Afterwards, they were further dried in an oven for 48 hours at 60° Celsius and were considered

fully dry as no change in weight was observed when they were weighed after 40, 44, and 48

hours. In addition to measuring EWT of each leaf sample (Equation (2.1)), LMA was measured

according to Equation (3.3). Reduced major axis regression was used to determine the NDI –

EWT relationship for each individual species, and also for all species combined.

5.3.2 Samples for validation of the EWT estimation

A total of 274 leaf samples were collected from twelve out of the thirteen trees shown in Figure

5-2, as the ash tree, labelled 7, was excluded from validation. Table 5-1 shows the number of

leaf samples collected from each tree. The leaf samples were collected from two canopy layers:

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the canopy top layer and the canopy bottom layer. This allowed the areas sampled for validation

to be explicitly identified in the TLS point cloud. The canopy top layer was 1 m above the

canopy walkway level (12 m), with a depth of one metre, while the canopy bottom layer

consisted of the low branches that were accessible from the ground. EWT and LMA of each

leaf sample were measured following the steps described in Section 5.3.1.

Table 5-1. Details of the species, locations and numbers of the leaf samples for the EWT

estimation validation. The samples from the ash tree, labelled 7, were excluded.

Tree label Species Number of leaf samples

Canopy top

layer

Canopy bottom

layer

1 Sycamore 20 18

2 Sycamore 18 10

3 Sycamore 20 20

4 Sycamore -- 20

5 Beech 19 --

6 Sycamore 20 --

8 Oak -- 24

9 Oak 20 --

10 Oak 20 --

11 Oak 15 --

12 Sycamore 15 --

13 Oak 15 --

Total number 182 92

5.4 TLS point cloud processing

5.4.1 Point cloud registration and filtering

The point clouds from each instrument were registered in Leica Cyclone, using the registration

targets, to build the forest plot. The registered P20 scans were then aligned to the registered

P40b scans. The outcome was a pair of P40b/P20 aligned point clouds at each scanning

positions. Points corresponding to ground and understory vegetation were removed to reduce

the size of the point clouds.

The P20 point cloud at each scanning position was filtered to match the same number of points

in the corresponding P40b point cloud using a nearest neighbour function as described in Section

3.3.3. As the nearest neighbour function tried to find the nearest neighbour in the P20 point

cloud to each point in the P40b point cloud, regardless of how far that nearest neighbour was,

further filtering was required. A threshold of 3 cm was applied, that is, any pair of P40b/P20

neighbour points > 3 cm apart was removed from the point cloud. The 3 cm threshold was

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chosen on the basis of 96% of the nearest neighbour distances being < 3 cm apart, while 99%

of distances were < 6 cm apart.

5.4.2 Generating the EWT point clouds

The filtered point clouds were calibrated to apparent reflectance and NDI was calculated for

each pair of P40b/P20 scans on a point-by-point basis. The NDI point clouds from the ten

scanning positions were merged into a single point cloud that covered the entire 35 × 45 m

rectangular plot. The sampled trees (Table 5-1) were manually extracted from the NDI point

cloud. They were divided into three groups according to their species, sycamore, oak and beech.

The species-specific NDI – EWT relationship found at leaf level, as described in Section 5.5.1

was applied to the NDI point clouds of each group, Equations (5.1), (5.2) and (5.3) for

sycamore, oak, and beech respectively, and EWT point clouds were generated. In addition, the

pooled NDI – EWT relationship that combined all species (Equation (5.5)) was applied to the

NDI point clouds of the twelve trees, regardless of their species, to investigate the possibility

of using a pooled EWT estimation model in a mixed-species forest plot without the need to

classify the trees according to their species.

5.4.3 Validating the EWT estimations

Visual inspection of the NDI point cloud and histogram of each tree showed that foliage NDI

was clustered around 0.3, while for wood it was clustered around zero. Wood typically is

expected to have higher SWIR reflectance than green foliage, as it contains less moisture, while

their NIR reflectance is expected to be similar at the wavelength used in this study (808 nm).

This caused the lower NDI values of wood components. Applying the NDI – EWT estimation

models, trained solely using green foliage, would then cause the majority of points

corresponding to woody materials to have EWT value equal to or below zero, even if they had

higher moisture in reality. The same applies to noise points, resulting from wrongly assigned

nearest neighbours, that is, a full hit being assigned to a partial hit, resulting in a very low or a

very high NDI value.

As the focus of this study was to estimate EWT of foliage only, a threshold of zero was used to

disregard the points corresponding to wood and noise. Afterwards, using visual inspection,

points that clearly corresponded to wood (being part of the trunk or primary branches) but which

were wrongly classified as leaves, were manually removed. In addition, it was possible to

visually identify and remove many of the points corresponding to lateral branches. However, it

was not possible to identify and remove smaller branches and twigs. Additionally, a Gaussian

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distribution was fitted to the EWT histogram and a threshold equal to twice the mean value was

applied to filter points with very high EWT (Figure 5-3).

Figure 5-3. Example of using the mean (µ) of the fitted EWT histogram Gaussian distribution

to remove the noise by applying a threshold equal to 2µ (purple).

The layers from which the leaf samples were collected were extracted from each individual tree

point cloud. The estimated EWT of each layer was compared to the actual EWT of the leaf

samples collected from that layer and the relative error was calculated according to

Equation (3.1).

The EWT point cloud of each tree was divided into a number of horizontal layers, each 1 m

deep, after removing the woody materials. EWT of each layer was plotted against the

corresponding height to produce the EWT vertical profile of the tree. The EWT vertical profiles

were produced from the EWT point clouds generated using the pooled EWT estimation model

(Equation (5.5)).


5.5.1 Leaf level results

For each individual species, moderate correlation was observed between NDI and EWT (R2 =

0.55, 0.57, 0.59 and 0.68 for the beech, ash, oak and sycamore species respectively, P < 0.05).

Some differences in the slope, and more obviously in the intercept, of the NDI – EWT

relationships were observed between the different species (Figure 5-4). This can be a result of

the remaining effects of the leaf internal structure on the NDI, as PROSPECT simulations

revealed that NDI can minimize but not entirely normalize such effects (Section 3.7.4). For

instance, Figure 5-4 shows that there are numerous leaf samples with EWT around

0.0095 g/cm2; however, they have NDI values varying between 0.21 and 0.25. Theoretically,

they should have had similar NDI values, if there were no remaining effects for the leaf internal

structure on NDI. Another factor that affected the slope and intercepts of the NDI – EWT

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relationships was the narrow range of EWT values within each individual species, especially in

beech, and it was not possible to determine the exact NDI – EWT relationship of each individual

species using such small EWT ranges. Thus, a dry-down experiment may be needed if the aim

was to determine the accurate NDI – EWT relationship for each species individually. The

species-specific NDI – EWT relationships can be described as:

EWT (g cm-2) = 0.0488 × NDI – 0.0016, for sycamore (5.1)

EWT (g cm-2) = 0.0553 × NDI – 0.0031, for oak (5.2)

EWT (g cm-2) = 0.0327 × NDI – 0.0002, for beech (5.3)

EWT (g cm-2) = 0.0534 × NDI – 0.0032, for ash (5.4)

Figure 5-4. The species-specific and pooled NDI – EWT relationships.

It was possible to fit a linear model to all leaf samples combined (R2 = 0.94, P < 0.05) (Figure

5-4). It is acknowledged that there remained a gap in the EWT values, between 0.0055 g/cm2

and 0.008 g/cm2, thus the high correlation can potentially be misleading as the samples were

not normally distributed. However, the consistency of trends between the general and individual

species models gave confidence it was suitable for application at the canopy scale. The pooled

NDI – EWT model can be described as:

EWT (g cm-2) = 0.0579 × NDI – 0.0039 (5.5)

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The leaf samples collected for the purpose of validating the EWT estimates revealed a vertical

heterogeneity in EWT within canopies. The average EWT of all leaf samples collected from

the canopy top layer was 20% higher than that of the leaf samples collected from the canopy

bottom layer (Figure 5-5). In addition, LMA of leaf samples from the canopy top layer was

42% higher than that of the canopy bottom layer, suggesting that the observed higher EWT in

the canopy top was caused by the leaves having higher LMA, increasing their ability to hold

moisture. To further investigate, the relationship between EWT and LMA was studied at leaf

level (Figure 5-6), revealing that EWT and LMA were highly correlated (R2 = 0.92, 0.61, 0.60

and 0.63 for beech, ash, oak and sycamore respectively), and that the relationship between EWT

and LMA was species-specific. Junttila et al. (2019) also reported that EWT and LMA were

highly correlated at the leaf level. Additionally, studying the EWT – LMA relationship within

each species revealed some differences between the individual trees (Figure 5-6).

Figure 5-5. A boxplot of the EWT of the leaf samples in canopy top and canopy bottom layers:

(a) all leaf samples combined, (b) sycamore, and (c) oak. The whiskers are the minimum and

maximum values.

Figure 5-6. The EWT – LMA relationships at leaf level.

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Figure 5-7. The EWT – LMA relationships of the individual trees: (a) sycamore and (b) oak.

Furthermore, the EWT – LMA relationship was investigated at the canopy level (Figure 5-8),

which further showed that EWT and LMA were highly correlated and that the relationship

between them was species-specific. Arellano et al. (2017) and Gara et al. (2018) also

investigated the relationship between EWT and LMA at canopy level using destructive

sampling, reporting that canopy layers with higher LMA had higher EWT.

Figure 5-8. The relationship between EWT and LMA at canopy level: (a) all species combined,

(b) Sycamore and (c) Oak.

5.5.2 EWT point clouds

The RMSE of the point cloud registration, reported by Leica Cyclone, was 3 mm for each

instrument separately. As the scans were conducted at a resolution of 3 mm at 10 m, the

registration accuracy was considered sufficient. The RMSE of the registration of the P20 point

clouds to the P40 point clouds was 1 mm. The high accuracy was a result of the similarities

between the two instruments in terms of their scanning mechanism and laser beam exit location,

and also a result of the similar scanning geometry. Another key factor was the absence of wind.

Scanning in more windy conditions would be expected to significantly reduce the registration

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accuracy (see the willow dataset, Section 6.3.2). The high registration accuracy allowed the

generation of 3D EWT point clouds, as shown in Figure 5-9 and Figure 5-10.

Figure 5-9. The 3D EWT distribution of the sampled trees.

Figure 5-10. Examples of the 3D EWT distribution of individual trees: (a) Sycamore tree,

labelled (6), and (b) Oak tree, labelled (11).

The point clouds revealed a significant difference between the leaf and wood EWT, showing

some potential of using the 3D EWT distribution in separating the leaf from the wood using

zero EWT as a separation threshold. However, testing this method revealed that many points

that clearly corresponded to wood (e.g. trunk, primary branches, lateral branches) had above

zero EWT, and thus were mistakenly classified as leaves. Attempting to filter these points using

a higher threshold resulted in removing points clearly corresponding to leaves. Thus, these

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points had to be removed manually, which rendered this leaf – wood separation method

impractical at the plot scale. In addition, it was not possible to visually identify and manually

remove misclassified points that corresponded to small branches and twigs. Additionally,

numerous points corresponding to leaves were also classified as wood, as they had below zero

EWT. This could be a result of wrongly assigned nearest neighbours, as discussed in Section

5.4.3. It was possible to filter these points using the statistical outlier removal tool in the

CloudCompare v. 2.6.2 software, as they were sparse points in comparison to the very dense

points in the trunk and branches. However, this method may also filter small branches and

twigs, and as no field measurements were conducted to validate this leaf-wood separation

approach, it was not possible to determine its accuracy.

5.5.3 Validating the EWT estimations

Comparing the estimated EWT to the actual EWT from the leaf samples revealed a relative

error of 7.7% on average in the EWT estimations for the species-specific models and 6.3% for

the pooled model. Table 5-2 summarizes all the observed errors. All errors were < 10%, except

for the errors obtained in the canopy top layer in sycamore trees number 2 and 12, and in the

canopy bottom layer in oak tree number 8, in addition to a high error (-21%) in the beech tree.

This high error in the beech tree can be a result of the narrow range of EWT in the leaf samples

used to build the EWT estimation model, which was insufficient to accurately determine the

slope and intercept of the NDI – EWT relationship. In the dry-down experiment (Chapter 4),

the Snake-bark maple species-specific model produced errors < 3% when applied at the canopy

level, mainly because the model covered a wide range of EWT values, being calibrated using

leaf samples collected while the canopies were drying down. This suggested that if the aim was

to use a species-specific model to estimate EWT at the canopy level, carrying out a dry-down

experiment would be recommended. This can be conducted by scanning the leaf samples and

measuring their weight once collected, then leaving them to dry naturally over a period of at

least one week, while scanning them and measuring their weight at fixed intervals. The leaves

can then be dried in the oven to measure their dry weight, then the NDI – EWT model can be

calibrated. This can ensure that the model covered a wide range of NDI and EWT values for

this species, which can then lead to a more accurate estimation of EWT at the canopy level

using the species-specific model.

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Table 5-2. EWT estimation errors in the twelve trees, for the canopy top and bottom layers. The

signs of the errors were ignored while calculating the average and total errors.

Tree Species

Relative error in EWT estimations

Species-specific models Pooled model

Canopy top Canopy bottom Canopy top Canopy bottom

1 Sycamore -5.9% 9.2% -5.3% 7.2%

2 Sycamore -13.5% 6% -13.5% 4.3%

3 Sycamore -2.5% 6.9% -2.7% 4.3%

4 Sycamore --- 9.3% --- 7.3%

5 Beech -21% --- -2.8% ---

6 Sycamore -0.7% --- -1% ---

8 Oak --- 12.5% --- 10.2%

9 Oak 8% --- 6.7% ---

10 Oak 3.7% --- 2.1% ---

11 Oak -0.6% --- -2.4% ---

12 Sycamore -12% --- -13.3% ---

13 Oak -3% --- -5.4% ---

Average error 7.1% 8.8% 5.5% 6.7%

Total error 7.7% 6.3%

When the pooled model was used, the error in the EWT estimation for the beech tree dropped

to - 2.8%, further showing that the species-specific model of beech was inaccurate. The errors

in sycamore trees number 2 and 12 slightly increased, with the errors being higher than those

observed in the remaining sycamore trees. As EWT was underestimated, this suggested lower

NDI values, which can be a result of the remaining effects of the leaf internal structure on the

NDI, if leaf samples from these two trees were thicker than the leaves used to build the EWT

estimation model, according to PROSPECT simulations. This was reflected in the average

LMA value of the leaf samples collected from the two trees, which was 0.0029 g cm-2, being

higher than the average LMA of the leaf samples collected from the remaining sycamore trees

(0.0021 g cm-2). On the other hand, the error in the EWT estimation in oak tree 8 dropped when

the pooled model was used, but remained higher than the errors observed in the remaining oak

trees. The overestimation of EWT suggested that leaf samples collected from that specific tree

were thinner than the leaf samples used to build the EWT estimation model. This was also

reflected in the average LMA of the leaf samples collected from the tree (0.0027 g cm-2), which

was lower than the average LMA of the leaf samples collected from the remaining oak trees

(0.0041 g cm-2). The observed EWT estimation errors showed the possibility of using a pooled

NDI – EWT model to successfully estimate EWT in a mixed forest plot without needing a NDI

– EWT estimation model for each individual species. Using a pooled EWT model can then be

more applicable as it does not require prior tree species classification. However, further

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experiments that include measuring leaf thickness are still needed to better understand the

source of the high errors observed in some trees.

5.5.4 EWT vertical profiles

The EWT vertical profiles (Figure 5-11) revealed a vertical variation in the EWT distribution

in all twelve trees, agreeing with the leaf sampling results (5.5.1). Figure 5-11 also shows the

advantage of using TLS data in mapping the EWT in forest plots over the destructive sampling

approach. TLS can estimate EWT in all canopy layers, which requires tree climbers and

extensive destructive sampling to be achieved using traditional approaches. Assuming that the

leaves in the top part of the canopy were sun leaves, and those in the bottom were shade leaves,

the vertical profiles of EWT showed a gradual transition between sun leaves and shades leaves,

with sun leaves having higher EWT, and correspondingly higher LMA, than shade leaves.

Figure 5-11. The EWT vertical profiles. Tree (5) is beech, trees (1, 2, 3, 4, 6 and 12) are

sycamore and trees (8, 9, 10, 11 and 13) are oak.

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All trees had higher EWT in the upper canopy than in the shaded lower canopy. The upper

canopy in all trees had an average of 24.2% more EWT than the lower canopy. However, the

errors presented in Table 5-2 showed that the EWT estimation model overestimated the EWT

in the canopy bottom layer and, in most cases, underestimated the EWT in the canopy top layer,

suggesting that the actual difference between EWT of upper and lower canopy can be higher

than 24.2%. The highest observed variation in EWT was in the beech tree, labelled (5), where

EWT in the upper canopy was 44% higher than the bottom canopy. The lowest variation was

observed in the oak tree, labelled (10), where EWT was 13.6% higher in the upper canopy than

in the lower. Similarities were observed in the vertical profiles of the sycamore trees, with the

upper canopy layer having an average of 20% higher EWT than the lower canopy. The vertical

profile of the beech tree was more distinctive. The vertical profiles of the oak trees showed

some variations from each other, with EWT being 25.4% higher in upper canopy than in lower

canopy, which suggested that the EWT vertical profile can vary within a species, depending on

each individual tree structure.

The vertical profiles observed in the forest plot concurred with the findings of Zhu et al. (2017),

Arellano et al. (2017) and Gara et al. (2018), all reporting higher EWT in the canopy top than

in the canopy bottom in a variety of species. Zhu et al. (2017) and Gara et al. (2018) studied

the EWT vertical profiles in small, individual trees from various species: weeping fig, Ficus

benjamina, Camellia japonica, Chamaedorea elegans, and Fatshedera lizei, in controlled

environments. On the other hand, Arellano et al. (2017) examined the EWT vertical

heterogeneity in forest plots in Amazonian forest in Ecuador. The results also agreed with the

vertical profiles observed in the dry-down experiment (Chapter 4). Typically, top layers of

canopy receive the majority of irradiance and trees tend to grow sun leaves in these layers to

optimize photosynthesis (Chazdon and Fetcher, 1984). Trees also tend to dedicate more

nutrients and water to sun leaves than to the shaded leaves in the canopy bottom for the same

purpose (Hirose and Werger, 1987; Hikosaka, 2004). The observed vertical distribution of EWT

can then be related to the distribution of sun/shade leaves within the canopy. As sun leaves

typically have higher LMA than shade leaves, and as EWT was found to be highly correlated

to LMA as discussed in Section 5.5.1, the EWT vertical profiles shown in Figure 5-11 also

reflected the vertical variation in LMA within canopies. Arellano et al. (2017) and Gara et al.

(2018) also showed that EWT and LMA vertical profiles showed similarities.

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5.6 Summary

This chapter was dedicated to investigate the possibility of using the NDI of the 808 nm NIR

wavelength and the 1550 nm SWIR wavelength to generate 3D EWT estimations at the canopy

level in a real forest environment. It described in detail a data collection campaign that took

place in a mixed deciduous forest plot in Wytham Woods, Oxford, UK, and resulted in mapping

the EWT in the forest plot in 3D.

At leaf level, moderate correlation was observed between NDI and EWT across four broadleaf

tree species: oak, sycamore, beech and ash. It was also possible to fit a pooled EWT estimation

model that combined all species, but more leaf samples still need to be added to the model to

fill the gap in the low EWT region of the model. At the canopy level, it was possible to achieve

a high registration accuracy for the point clouds, despite the difference in the laser beam

footprint and beam divergence between the two instruments. This was a result of the similarity

in the chassis of the instruments and their laser beam exit locations, in addition to the similarity

in the scan geometry. NDI was successfully used to generate 3D estimations of EWT in the

scanned forest plot, using species-specific models in addition to a pooled EWT model, with a

relative error of 7.7% and 6.3% in the EWT estimation respectively.

The generated 3D distributions of EWT revealed some vertical heterogeneity in all the sampled

trees. All the trees were found to have higher EWT in the canopy top than the canopy bottom,

with EWT gradually becoming lower towards the canopy bottom. Such variation in EWT can

be a result of the leaves in the top of the canopy, predominantly sun leaves, having higher LMA

than shaded leaves in the bottom of the canopy, as EWT and LMA were found to be highly

correlated. The observed EWT vertical variation in the forest plot may affect the estimation of

EWT using passive optical spaceborne or airborne sensors, because measurements from such

instruments will be dominated by the canopy top, which, according to this study, has higher

EWT than the lower layers in the canopy. This was further investigated in Chapter 7 using

radiative transfer modelling.

89

Chapter 6. Transferability of the EWT estimation approach to different

sites

6.1 Introduction

Chapter 4 showed the potential of using NDI of the 808 nm and the 1550 nm wavelengths to

generate 3D estimates of EWT at the canopy level, with errors < 4%. However, the experiment

was conducted in a controlled indoor environment, using healthy individual trees, and with no

effect of wind or occlusion. When the method was transferred to a real forest environment, as

shown in Chapter 5, EWT was successfully estimated with errors < 8%. The higher errors were

caused by the lower registration accuracy because of the slight movements of trees due to wind,

in addition to occlusion, remaining partial hits, remaining effects of leaf internal structure, and

the remaining woody materials that could not be filtered manually. Nevertheless, the conditions

in which the data collection campaign took place were considered very suitable for laser

scanning, as strong wind was absent.

This chapter is dedicated to investigating the transferability of the EWT estimation approach to

two additional sites with more challenging conditions. The first site consisted of six willow

(Salix spp.) bioenergy crop plots and was scanned to study the effects of wind and senescence

of leaves on the accuracy of the EWT estimation. The second site was a mixed-species urban

tree plot that was scanned twice, at the end of a heatwave and two months later, to further

investigate the leaf senescence effect and also to study the ability of the EWT estimation

approach to detect temporal changes in EWT. Furthermore, the ability of NDI to directly

estimate FMC at the canopy level was investigated, as a proof-of-concept for future work. The

results of the willow dataset have been published in Elsherif et al. (2019b). The results of

Exhibition Park dataset have been published in Elsherif et al. (2019a). The 3D FMC results

have been published in Elsherif et al. (2019c).

6.2 Willow dataset

6.2.1 Study area

The study area was at the Newcastle University Cockle Park Farm in Ulgham, Northumberland,

UK (Figure 6-1). Cockle Park Farm is approximately 262 hectares and has been part of

Newcastle University since 1896. It is a working farm that offers a unique opportunity for

interdisciplinary research in agriculture, including food security and the generation and efficient

use of bioenergy. The data collection took place in part of the 10 hectares devoted to short

90

rotation coppiced willow crops (55.2° N, 1.69° W) (Figure 6-1) which are used for bioenergy

generation. The 10 hectares are divided into four main plots, with each plot containing thirteen

subplots planted with different willow varieties, with a nominal width of 6 m each (Figure 6-2).

Six willow subplots of different varieties were chosen for the data collection, labelled as

Endeavour (E), Terra Nova (TE), Beagle (B), Stott (S), Tordis (T), and Nimrod (NI) (Figure

6-2). As the data collection took place in October, the leaves were already senescent, as shown

in Figure 6-3.

Figure 6-1. The study area and the location of the 10 hectares devoted to the willow crops,

indicated by red.

Figure 6-2. The willow subplots and varieties, coloured by their harvested weight (Kg) in March

2015, after four years of growth. Shaded area (a) indicates the six willow subplots used in the

data collection. The figure was adapted from Gaulton et al. (2015).

Cockle Park

Hebron

(a)

624 m

91

Figure 6-3. The willow varieties. All leaves were already senescent.

6.2.2 Experiment setup

Two scanning positions were set at a range of 15 m away from the plots, covering three adjacent

plots each. The first scanning position covered plots ‘E’, ‘TE’ and ‘B’, while the second covered

plots ‘S’, ‘T’ and ‘NI’. The plots were scanned from one side from each scanning position with

the P40a and P20 scanners. The scans were conducted with a resolution of 3 mm at 10 m. Four

Leica black and white registration targets were placed in each scan to be used in aligning the

P20 point cloud to the P40a point cloud. In addition, reflectors were mounted on wooden sticks

and used to mark the approximate boundaries of each willow plot. The scans were conducted

in windy conditions. Figure 6-4 shows a view of the plots covered from scan position one. Table

6-1 shows the height and width of the scanned side of the plots. Figure 6-5 shows the P20 point

clouds collected from the two scanning positions.

Figure 6-4. A view of the three plots covered from scanning position one, left is plot ‘E’, middle

is plot ‘TE’, and right is part of plot ‘B’. Three out of four Leica black and white registration

targets can be seen, in addition to the reflectors used to mark the approximate boundaries of

each plot.

(B) (E) (TE) (NI) (T) (S)

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Table 6-1. Height and width of the scanned side of the plots.

Plot E TE B S T NI

Height (m) 4.7 4.1 3 3 4.5 3.5

Width (m) 5.5 6.3 5.6 6 7.7 5.2

Figure 6-5. The P20 point clouds of scan position one (top) and scan position two (bottom).

The point clouds show only the front side of the plots, 1 m deep into the canopy. The colour

ramps show the uncalibrated intensity (dimensionless).

6.2.3 Leaf sampling and biochemistry measurements

For each plot, six leaf samples were collected randomly to be used in building the EWT

estimation model. Six additional leaf samples were collected from a sampling layer with a

thickness of one metre, located 1.5 m above ground, for the validation of the EWT estimation.

The total number of leaf samples collected was 72 samples. The leaf samples were processed

as described in Section 3.3.4 to measure their EWT.

To build the EWT estimation model, the six leaf samples collected for this purpose from each

plot were suspended in a wooden frame and scanned by the P20, followed by the P40a, at a

range of 7 m. The wooden frame was normal to the laser beam direction to minimize the

incidence angle effects. The intensity values of each leaf were calibrated to apparent

Z (

m)

Plot (B) Plot (TE) Plot (E)

X (m)

Z (

m)

Plot (S) Plot (T) Plot (NI)

93

reflectance, using the calibration models described in Section 3.7.1. NDI was calculated for

each leaf following Equation (2.5). NDI values of the leaf samples were plotted against EWT

values to determine the NDI – EWT relationship for each plot.


The P20 point clouds were aligned to the P40a point clouds in Leica Cyclone using the

registration targets. Afterwards, the aligned point clouds were calibrated to apparent reflectance

on a point-by-point basis, and reflectance point clouds were generated, using the calibration

models described in Section 3.7.1. The reflectance point clouds were used to estimate EWT of

each plot using two different approaches.

The first approach followed the steps described in Section 3.3.3 and NDI point clouds were

generated on a point-by-point basis. Afterwards, the NDI - EWT estimation model of each

willow variety was used to generate EWT point cloud of each plot. The layer from which leaf

samples were collected for validation were then extracted from the EWT point cloud of each

plot. Removing the woody materials and cleaning the noise followed the same method used in

the forest dataset, as described in Section 5.4.3. Estimated EWT of each layer was compared to

actual EWT of the leaf samples collected from that layer to evaluate the accuracy of the

estimation.

Due to the strong wind during scanning, which was expected to have severe effects on the

accuracy of the point cloud registration and the estimation of EWT on a point-by-point basis, a

second approach to estimate EWT was also used. The approach aimed at estimating the average

EWT of the canopy layer from which leaf samples for validation were collected. For each plot,

the sampled canopy layer was extracted from the P20 and the P40a point clouds, and average

reflectance of the layer was calculated for each wavelength. Average NDI of the layer was

calculated using the average P20 and P40a reflectance values instead of calculating NDI on a

point-by-point basis. The average EWT of the layer was estimated using the average NDI value.

To validate the estimation, the sampled layer estimated EWT of each plot was compared to the

mean EWT of the leaf samples collected from that layer.

6.3 Willow dataset results and discussion

6.3.1 Leaf level

Moderate to high correlation was observed between NDI and EWT (Figure 6-6 and Table 6-2).

The correlation was significant (P < 0.05) except for plot ‘S’. Some differences in the slopes

and intercepts of the NDI – EWT relationships of the different plots were observed, with the

94

most significant differences being in plots ‘E’ and ‘S’ (Table 6-3). For plot ‘E’, the NDI – EWT

relationship had clearly different slope and intercept in comparison to the remaining plots. This

significant difference can be a result of leaf internal structure effects, if the two leaves that had

the highest and lowest EWT values were at different levels of senescence in comparison to the

remaining four leaves. Another reason can be a laboratory measurement error in the two leaf

samples corresponding to the lowest and highest EWT.

Figure 6-6. The relationship between NDI and EWT of each willow plot.

Table 6-2. The correlation between NDI and EWT.

Plot E TE B S T NI

R2 (NDI – EWT),

P < 0.05 0.66 0.82 0.64 0.62 0.76 0.72

Table 6-3. Slopes and intercepts of the NDI – EWT relationships.

Plot E TE B S T NI

Slope 0.1187 0.0337 0.0514 0.0336 0.0457 0.0609

Intercept -0.0140 0.0051 0.0011 0.0086 0.0039 0.0006

For plot ‘S’, the intercept was different than the other plots. The leaf samples had lower NDI

than the leaf samples from other varieties that had almost the same EWT. According to

PROSPECT simulation results (Section 3.7.4), this can be a result of the leaf samples having

higher N, corresponding to a significantly different mesophyll structure. However, in healthy

leaves, higher N usually corresponds to higher LMA (Jacquemoud and Baret, 1990). Higher N

would cause a decrease in NDI, while higher LMA would cause an increase in NDI. If the two

95

effects were almost similar, a slight change in NDI would be observed between species. The

significant decrease in NDI for plot ‘S’ suggested that N had a much more substantial effect on

NDI than LMA, concurring that leaf samples from plot ‘S’ had significantly different mesophyll

structure than the remaining leaves. A possible reason for this can be that the leaves were more

senescent, as the more senescent the leaves were, the higher their N value would be

(Jacquemoud and Baret, 1990).

The variety-specific NDI – EWT relationships can be described as:

EWT (g cm-2) = 0.1187 × NDI – 0.0140, for plot ‘E’ (6.1)

EWT (g cm-2) = 0.0337 × NDI + 0.0051, for plot ‘TE’ (6.2)

EWT (g cm-2) = 0.0514 × NDI + 0.0011, for plot ‘B’ (6.3)

EWT (g cm-2) = 0.0336 × NDI + 0.0086, for plot ‘S’ (6.4)

EWT (g cm-2) = 0.0457 × NDI + 0.0039, for plot ‘T’ (6.5)

EWT (g cm-2) = 0.0609 × NDI + 0.0006, for plot ‘NI’ (6.6)

It was also possible to fit a pooled NDI – EWT model for all leaf samples combined, excluding

plot ‘S’ (R2 = 0.57, P < 0.05), which can be described as follows:

EWT (g cm-2) = 0.0532 × NDI + 0.0020 (6.7)

Both the variety-specific models and the pooled model were used to estimate EWT at the

canopy level.

6.3.2 Canopy level

RMSE of the P40a/P20 point cloud registration for scan positions one and two, reported by

Leica Cyclone, was 1.6 cm and 1.7 cm respectively. Such errors were considered high in

comparison to the errors obtained in the indoor experiment (Chapter 4, RMSE = 0.8 mm) and

the forest dataset (Chapter 5, RMSE = 3 mm). The lower registration accuracy was a result of

the wind effect. The errors in the registration severely affected the accuracy of retrieving EWT

on a point-by-point basis, and very high errors were observed, using the variety-specific models

and also the pooled EWT model (Table 6-4). The errors were a result of the nearest neighbour

function, while attempting to calculate NDI on a point-by-point basis, wrongly assigning the

nearest neighbours because of leaf and branch movement between the scans. Eighty percent of

the selected nearest neighbours were < 3 cm apart, while only 36% were < 1 cm apart. In the

96

indoor experiment (Chapter 4), all the nearest neighbours were < 3 cm apart and 99.6% of them

were < 1 cm apart, while in the forest dataset (Chapter 5), 96% were < 3 cm apart, and 80%

were < 1 cm apart.

Table 6-4. Errors in the EWT estimation. Approach one refers to estimating EWT on a point-

by-point basis, while approach two refers to using the average NDI to estimate the average

EWT.

When calculations were not done on a point-by-point basis and the average NDI was used to

estimate the average EWT, the average errors in the EWT estimation dropped to 8.9% and 6.6%

for the variety-specific and pooled models respectively (Table 6-4). For plot ‘E’, the error

obtained using the pooled model was much lower than the error obtained using the variety-

specific model (6.4% and -17.6% respectively). This suggested that the variety-specific NDI –

EWT relationship for plot ‘E’, which had different slope and intercept than the remaining plots,

was either influenced by the leaf samples being at different levels of senescence, or was caused

by laboratory measurement error. High correlation was observed between the average estimated

EWT of the sampled layer in each plot and the actual EWT of the leaf samples collected from

the layer, using both the variety-specific models and the pooled model (R2 = 0.73,

RMSE = 0.0013 g/cm2 and R2 = 0.62, RMSE = 0.001 g/cm2 respectively) (Figure 6-7). The

correlation was significant in both cases (P < 0.05).


Approach one Approach two

Plot Variety-specific

model Pooled model

Variety-specific

model Pooled model

NI 45.2% 23.7% -10.4% -10.1%

T 24.7% 15.2% -4.2% -9%

S 10.5% N/A -9.3% N/A

B 26.3% 21.3% -9.1% 0.5%

TE 23.3% 27.4% 2.6% 7.1%

E 52.2% 24.8% -17.6% 6.4%

Average 30.4% 22.5% 8.9% 6.6%

97

Figure 6-7. The relationship between the estimated EWT of the sampled layers and the actual

EWT: (a) for the variety-specific models and (b) for the pooled model.

No significant difference in reflectance between leaf and wood was observed in the P40a point

clouds of all plots, as shown in Figure 6-8b for plot ‘T’ as an example. This can be a result of

the wood having high water content as willows are known to have bark filled with watery sap,

which is used in numerous medical applications (Orémusová et al., 2012). The EWT point

cloud of the plot (Figure 6-8d) concurred with this observation and showed that the wood had

higher EWT than leaves. This agreed with the results obtained for the young, deciduous trees

in Chapter 4 and showed that the SWIR wavelength may not be suitable for leaf/wood

separation if the bark was green or had high water content. Attempting to extract the woody

materials from the EWT point cloud using a threshold by trial and error resulted in some points

corresponding to leaves being classified as wood (Figure 6-8f). On the other hand, clear

difference in reflectance between leaf and wood was observed in the P20 point cloud (Figure

6-8a) as wood had higher reflectance than leaves. Attempting to extract the wood using a

threshold produced a visually-better result than using EWT, as fewer points corresponding to

leaves were misclassified (Figure 6-8e). This showed the potential of using the 808 nm NIR

wavelength in leaf/wood separation in willows, which can be useful in biomass estimations.

(b) (a)

98

Figure 6-8. Plot ‘T’ point clouds: (a) the P20 reflectance, (b) the P40 reflectance, (c) NDI point

cloud, (d) EWT point cloud, (e) woody materials extracted from the P20 reflectance point cloud

using 0.35 threshold and (f) woody materials extracted from EWT point cloud using 0.03 g/cm2

threshold. Thresholds were chosen by trial and error until changing the threshold did not

visually improve the results.

6.4 Exhibition Park dataset

6.4.1 Study area

The study area was a mixed-species tree plot (35 m × 39 m) in Exhibition Park, Newcastle upon

Tyne, UK (54.98° N, 1.62° W) (Figure 6-9). The tree species included: Ilex aquifolium (holly),

Acer pseudoplatanus (sycamore), Sorbus intermedia (Swedish whitebeam), Fraxinus excelsior

(ash), Aesculus hippocastanum (horse chestnut), Fagus sylvatica (beech), and Tilia x europaea

(lime). A single scanning position was set in the centre of the plot, corresponding to a wide gap

in canopy cover, aimed at obtaining as many laser beam returns as possible from the canopy

top. The scanning position covered nine trees: two holly trees, two ash trees, two Swedish

Z (

m)

X (m)

Z (

m)

Z (

m)

X (m)

(g/cm2)

(a) (b)

(c) (d)

(e) (f)

99

whitebeam tress, one beech tree, one sycamore tree, and one horse chestnut tree. The horse

chestnut tree was suffering from horse-chestnut leaf miner (Cameraria ohridella) and thus was

excluded from any further processing.

Figure 6-9. The Exhibition Park dataset study area: (a) Exhibition Park and (b) the scanned tree

plot.

The plot was scanned on 7th of August 2018, at the end of the 2018 heatwave that hit the British

Isles between 23rd of June and 7th of August, as part of the 2018 European heatwave, making

summer 2018 the joint-warmest summer in recorded in the UK, and the second warmest

summer in the North East England region. Temperatures in Newcastle upon Tyne reached 26

°C, significantly higher than the 15 °C recorded average summer temperature. Scans were

conducted with the P20 and the P50 instruments mounted consecutively on the same tripod, on

the same surveying point (scanning position). Three Leica black and white registration targets

were placed in the plot at different heights for the purpose of aligning the P20 and P50 point

clouds. A full-hemisphere scan (360° × 270°) was conducted with each instrument with a

resolution (point spacing) of 3 mm at 10 m. The duration of the scan was approximately fifteen

minutes for each instrument. The plot was scanned again on 22nd of October 2018 while leaves

were senescing, using the same scanning set-up. Average temperature in October was 13 °C,

with periods of rainfall throughout the month. On each date, leaf samples were collected

immediately after scanning to link the TLS data to EWT, as described in Section 6.4.2.

6.4.2 Leaf sampling and biochemistry measurements

On each date, two sets of leaf samples were collected, one for the purpose of building the NDI

– EWT estimation model, and the other for the validation of the EWT estimates. As the study

Newcastle upon Tyne

Exhibition Park

772 m

53 m

100

area was in a public park, extensive destructive sampling of the trees was not possible. The total

number of leaf samples collected in August dataset was 50 samples, while the total number of

samples collected in October dataset was 38 samples. Leaf sampling details are given in Table

6-5. Samples for building the EWT estimation model were collected randomly from the plot.

On the other hand, leaf samples for validation were collected from a small volume,

approximately 0.5 m × 0.5 m × 0.5 m, with a known crown location in a specific tree from each

species. Sycamore leaf samples were found to be covered with grey powdery material,

indicating that the tree suffered from powdery mildew disease. This severely affected the NDI

– EWT relationship as discussed in Section 6.5.1, and thus the tree was excluded in October

data collection. Leaf samples of holly were thicker than the other species, in addition, they had

glossy, waxy surface. The lime tree was on the edge of the plot, fully occluded by two ash trees,

thus, no leaf samples were collected for validation and samples were only collected to add

species variety to the EWT estimation model in August. In October, the tree had already lost its

leaves.

Table 6-5. Leaf samples collected to build the EWT estimation model and validate the

estimation in August and October datasets.

Date August October

Type of samples EWT model Validation EWT model Validation

Swedish Whitebeam 5 5 5 5

Ash 3 5 5 4

Beech 6 5 5 5

Holly 4 5 4 5

Sycamore 3 4 --- ---

Lime 5 --- --- ---

Total number of leaf

samples 26 24 19 19

The fresh weight and surface area of each leaf sample was measured as described in Section

3.3.4. The leaf samples were then left to dry naturally over a period of five days (a week for

October dataset), then were further dried in an oven for 72 hours at 60 °C. Holly leaf samples

were oven-dried for an additional six days until no change in their weight was observed,

ensuring that they were fully dry. The dry weight of each leaf sample was measured using the

same scale used to measure the fresh weight, and EWT was calculated according to Equation

(2.1).

101

Samples for building the EWT estimation model on each date were scanned by both the P20

and P50, immediately after measuring their fresh weight, at ranges of 6 m and 7 m for August

and October datasets respectively. NDI was calculated for each leaf after calibrating the

intensity values to apparent reflectance using the calibration models described in Section 3.7.1.

For the August dataset, different pooled NDI – EWT relationships were examined, in addition

to species-specific relationships. Firstly, a pooled NDI – EWT model was fitted to all species

combined. Next, the diseased sycamore leaf samples were excluded, and a second pooled NDI

– EWT model was fitted to the remaining species. The next step was to exclude holly leaf

samples from the pooled EWT model to account for their thickness and surface characteristics

that differed from the remaining species. The remaining leaf samples were then combined with

the leaf samples measured in Chapter 4, and a third pooled NDI – EWT model was fitted in an

attempt to develop a species-independent EWT estimation model for leaves from park

environments, collected from different sites. For the October dataset, a pooled NDI – EWT

model was fitted to leaf samples from all species combined. In addition, holly leaf samples were

excluded, and a second pooled EWT model was fitted to the remaining species. Furthermore,

the leaf samples were combined with the willow leaf samples (Section 6.2.3) in an attempt to

derive a site- and species-independent NDI – EWT relationship for senescent leaves.


The P20 and P50 point clouds on each date were aligned in Leica Cyclone. NDI point cloud of

the plot on each date was generated on a point-by-point basis (Section 3.3.3). Individual trees

were then manually extracted and species-specific and pooled NDI – EWT models on each date

were used to generate the EWT point clouds. The woody materials were removed as described

in Section 5.4.3. To validate the EWT estimates, the sections from which leaf samples for

validation were collected (0.5 m × 0.5 m × 0.5 m volume) were extracted from the

corresponding trees in the EWT point clouds and the estimated EWT was compared to the

actual EWT measured from destructive leaf sampling and relative errors in the EWT

estimations were calculated.

The temporal change in EWT (increase/decrease) between August and October was measured

for each tree from the TLS estimated EWT, and also from the destructive sampling EWT, to

investigate the accuracy of TLS in detecting such changes. Afterwards, the EWT point-cloud

of each tree was split into multiple horizontal layers, 1 m each. Average EWT of each layer was

calculated and plotted against height at the centre of the layer to produce a vertical profile of

EWT distribution in the canopy. The EWT vertical profiles produced for each tree in August

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and October datasets were compared, to study how the vertical distribution of EWT varied

temporally.

6.5 Exhibition Park dataset results and discussion

6.5.1 Leaf level

A significant correlation (P < 0.05) was found between NDI and EWT for each species on both

dates (Table 6-6 and Figure 6-10). Some differences in slopes and intercepts of the NDI – EWT

relationships were observed between the different species, being a result of the remaining leaf

internal structure effects on NDI and/or the small number of leaves used to derive the NDI –

EWT relationships, which was insufficient to accurately determine the correct slopes and

intercepts.

On both dates, the trendline of the NDI – EWT relationship of holly was significantly shifted

up in comparison to the other species, similar to what was observed for plot ‘S’ in the willow

dataset. As discussed in Section 6.3.1, this can be a result of a significantly different leaf internal

structure of holly in comparison to the other species. Holly leaves were clearly thicker than the

leaves of the other species, and although their leaf thickness was not measured, their average

LMA (0.0164 g cm-2) was 129 % higher than average LMA of ash leaf samples (0.0071 g cm-

2), 117 % higher than average LMA of Swedish whitebeam leaf samples (0.0075 g cm-2), and

192 % higher than average LMA of beech leaf samples (0.0056 g cm-2). Gaulton et al. (2013)

reported a similar observation when two Fallopia japonica (Japanese knotweed) leaf samples

were attached together to form a sample with double thickness and were found to deviate from

the NDI – EWT relationship of the remaining species. It is worth mentioning that the shiny

surface of holly leaf samples could also have been a factor that contributed to the deviation of

the NDI – EWT relationship of this species. Zhu et al. (2017) showed that at 1550 nm

wavelength, shiny leaves had stronger specular reflection than matt leaves at zero incidence

angle. Higher reflectance at the 1550 nm wavelength would reduce the NDI value, causing the

upward shift of the trendline of the NDI – EWT relationship of holly in comparison to the other

matt leaves.

Table 6-6. The correlation between NDI and EWT for the species-specific models.

Beech Swedish Ash Holly Sycamore Lime

R2 (August) 0.65 0.94 0.92 0.67 0.85 0.88

R2 (October) 0.93 0.59 0.76 0.74 N/A N/A

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Figure 6-10. Species-specific NDI – EWT relationships: (a) August and (b) October.

The NDI – EWT relationship of diseased sycamore leaves appeared to be affected by the low

NDI value (0.08) of one sycamore leaf. Another sycamore leaf also had lower NDI value than

leaves from other species with similar EWT. The two leaves had lower NIR reflectance, 0.31

and 0.36 respectively, than all the other leaf samples, which had NIR reflectance between 0.43

and 0.56. On the other hand, the SWIR reflectance of the two leaves, 0.26 and 0.24 respectively,

was within the minimum and maximum values observed in the leaf sampling, 0.19 and 0.35

respectively. The low NDI values were therefore a result of the low NIR reflectance, which

could have been caused by the powdery mildew that covered the leaves’ surface. Yuan et al.

(2014) reported similar observation in winter wheat, showing a decrease in NIR reflectance and

a slight increase in SWIR reflectance in leaves infected with powdery mildew in comparison to

healthy leaves. In general, diseases are known to reduce leaf reflectance in NIR (Nilsson, 1991),

and NIR wavelengths have been adopted in detecting powdery mildew infections in winter

wheat (Zhang et al., 2012; Yuan et al., 2014) and grape (Beghi et al., 2017). However, powdery

mildew in wheat is caused by different fungus than in sycamore and further investigation is still

needed by scanning healthy and diseased sycamore leaves.

The species-specific NDI – EWT models can be described as follows for August dataset:

EWT (g cm-2) = 0.0289 × NDI + 0.0006, for beech (6.8)

EWT (g cm-2) = 0.0362 × NDI + 0.0002, for Swedish whitebeam (6.9)

EWT (g cm-2) = 0.0711 × NDI – 0.0088, for ash (6.10)

EWT (g cm-2) = 0.0836 × NDI – 0.0083, for holly (6.11)

(a) (b)

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EWT (g cm-2) = 0.0166 × NDI + 0.0073, for sycamore (6.12)

EWT (g cm-2) = 0.0287 × NDI + 0.0010, for lime (6.13)

The species-specific models are as follows for October dataset:

EWT (g cm-2) = 0.0366 × NDI + 0.0017, for beech (6.14)

EWT (g cm-2) = 0.0632 × NDI – 0.0034, for Swedish whitebeam (6.15)

EWT (g cm-2) = 0.0492 × NDI + 0.0006, for ash (6.16)

EWT (g cm-2) = 0.0558 × NDI + 0.0042, for holly (6.17)

For the August dataset, it was possible to fit a pooled NDI – EWT model for all leaf samples

combined (R2 = 0.7, P < 0.05) (Figure 6-11). However, the trendline appeared to be affected by

the low NDI values of the sycamore leaves. Excluding the sycamore leaves and fitting a second

pooled EWT model improved the correlation (R2 = 0.88, P < 0.05) (Figure 6-12, Equation

(6.18)). Similarly, a linear model was fitted to all leaf samples combined in the October dataset

(R2 = 0.93, P < 0.05) (Figure 6-12, Equation (6.19)). Comparing the pooled EWT models on

the two different dates showed similarity in the slopes, but a difference in the intercept, with

the trendline of the October model being shifted up in comparison to the trendline of the August

model. This is likely to be a result of the difference in leaf internal structure between green

leaves (August) and senescent leaves (October), as senescence changes leaf cell structure

(Jacquemoud and Baret, 1990).

Figure 6-11. Pooled NDI – EWT model for August dataset. The trendline was affected by the

Sycamore low NDI values.

105

Figure 6-12. Pooled NDI – EWT models for August (bottom), excluding the Sycamore leaves,

and for October (top). A shift can be seen between the two trendlines, caused by the leaf

senescence.

The pooled EWT models can be described as follows:

EWT (g cm-2) = 0.0925 × NDI – 0.0131, for August (6.18)

EWT (g cm-2) = 0.0852 × NDI – 0.0076, for October (6.19)

Additionally, the leaf samples collected in August were combined with the leaf samples

collected in the indoor dry-down experiment described in Chapter 4, in an attempt to find a

species-independent EWT estimation model for healthy leaves in park environments. However,

a single model could not be fitted accurately without excluding the holly and the sycamore leaf

samples (Figure 6-13). This concurred with the observation that the holly leaf samples did not

follow the same NDI – EWT relationship of the other species, as a result of their thickness or

different surface characteristics, and neither did the diseased sycamore leaves, as a result of

their different surface characteristics. In a similar manner, the holly leaf samples in the October

dataset were excluded, and a new pooled EWT model was fitted to the remaining species

(Figure 6-14). The pooled EWT model was shifted up in comparison to the model for the

August dataset as a result of the leaf senescence (Figure 6-14). The pooled EWT models after

excluding the holly leaf samples can be described as:

EWT (g cm-2) = 0.0631 × NDI – 0.0064, for August (6.20)

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EWT (g cm-2) = 0.0576 × NDI – 0.0019, for October (6.21)

Figure 6-13. Pooled NDI – EWT model for August leaf samples combined with leaf samples

collected in the indoor dry-down experiment (Chapter 4). Two of the diseased Sycamore leaves

appear as outliers, while the Holly leaf samples have their own EWT model.

Figure 6-14. Pooled NDI – EWT models for August (bottom) and for October (top), after

excluding Holly leaf samples. The shift in trendlines of the NDI – EWT relationships was

caused by the leaf senescence.

107

Next step was to investigate the possibility of fitting a site- and species-independent EWT

estimation model for senescent leaves. For this, the leaf samples from October dataset were

combined with the willow leaf samples (Figure 6-15). This showed a difference in the slopes

and intercepts of the NDI – EWT relationships between the willow leaf samples and October

dataset. The trendlines of willow were shifted up, suggesting that the willow leaves were more

senescent than the October dataset leaves. Additionally, some willow leaves, including a leaf

from plot ‘B’, a leaf from plot ‘TE’, and three leaves from plot ‘E’, appeared to be following

the NDI – EWT relationship of October dataset more than that of the willow dataset.

Furthermore, it seemed to be possible to fit an NDI – EWT model for plot ‘S’ and Holly leaf

samples combined. These observations revealed that the NDI – EWT relationship may not be

dependent on the species as much as it is dependent on the internal structure of leaves. It was

not possible to fit a general EWT estimation model for senescent leaves, as they appeared to be

at different levels of senescence.

Figure 6-15. Pooled NDI – EWT models for October dataset (bottom), for willow dataset

(middle), and for willow plot ‘S’ and Holly leaf samples (top).

6.5.2 Canopy level

The visual inspection of the EWT point cloud of the plot in both dates, and the histogram of

EWT distribution, showed differences in EWT between species, suggesting that the EWT point

cloud can be useful in species classification in mixed tree plots (Figure 6-16). Using the species-

specific models to estimate EWT at canopy level resulted in relative errors in the

estimation < 10% in all species in both dates, except for beech and sycamore in the August

dataset (Table 6-7). The errors in the EWT estimation for each species varied between the two

different dates, being higher in the August dataset than in the October dataset, except for holly.

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This concurred with the observations obtained in the forest dataset (Section 5.5.3) that the

accuracy of using species-specific models to estimate EWT at the canopy level depended

mainly on how accurate the slope and intercept of the model were. For the sycamore tree, severe

error was observed in the EWT estimation (- 31.5%). This suggested that the species-specific

model derived at the leaf level did not represent the NDI – EWT relationship at the canopy

level.

Figure 6-16. Top view of the EWT point cloud of the plot in August and the approximate

boundaries of the trees: (a) Swedish Whitebeam tree 1, (b) Swedish Whitebeam tree 2, (c) Ash

tree 1, (d) Ash tree 2, (e) Beech tree, (f) Holly tree 1, (g) Holly tree 2, (h) Horse chestnut tree

and (i) Sycamore tree. * indicates that samples were collected form the tree for EWT validation.

Table 6-7. The errors in EWT estimations for the species-specific models, pooled model 1,

which refers to the pooled EWT model with holly leaf samples included, and pooled model 2,

which refers to the pooled EWT model without holly leaf samples.


August dataset October dataset

Species Species-

specific

Pooled

model 1

Pooled

model 2

Species-

specific

Pooled

model 1

Pooled

model 2

Swedish

Whitebeam -9.6% 23.4% -2.2% -1.2% 17.8% 6.3%

Ash -3.2% 25.5% -4% -1% -4.2% -5.6%

Beech -14.1% 56.2% 12% 8.5% -17.6% 7.3%

Holly -4.4% 1% --- 5.8% 2.4% ---

Sycamore -31.5% -46.7% --- --- --- ---

(a)*

(i)*

(h)

(g)* (f)

(e)*

(b)

(c)

(d)*

(g/cm2)

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Using the pooled EWT model that included the holly leaf samples, Equations (6.18) and (6.19)

for August and October datasets respectively, resulted in severe errors in the EWT estimation

in both dates, except for the ash tree in October and the holly tree in both dates (Table 6-7).

This revealed that although the models had high fitting accuracy (R2 = 0.88 and 0.93 for August

and October datasets respectively), the models did not represent the NDI – EWT relationship

at the canopy level, except for the holly species, and thus produced significant errors in the

EWT estimation. When the pooled EWT models that excluded the holly species were used,

Equations (6.20) and (6.21) for the August and October datasets respectively, the errors dropped

significantly (Table 6-7). Thus, care must be taken while fitting a pooled EWT estimation model

that is to be applied at the canopy level in a mixed-species plot, and species that have thicker

leaves or different surface characteristics than the remaining species need to be excluded from

the model.

In the case of the diseased sycamore tree, both pooled EWT models failed to correctly estimate

EWT at the canopy level, with the first model producing severe error in the EWT estimation

(- 46.7 %), and the second model producing a below zero EWT value. This suggested that this

EWT estimation approach can be inapplicable if leaves were covered with a material that did

not affect the two wavelengths included in NDI in a similar manner. However, such diseased

trees will appear as outliers in the NDI – EWT relationship in comparison to the healthy trees

and thus, this approach can help in detecting diseased trees in tree plots. Following the

unsuccessful attempts to estimate EWT of the sycamore tree, the tree was not included in

October dataset.

Overall, the errors in August were less than the errors in October, except for the beech tree,

which may have been a result of leaf senescence. Senescence effect on leaf internal structure is

known to vary between species, and can also vary between leaves from the same species if they

were at different levels of senescence (Buchanan-Wollaston, 1997). Thus, it can be more

challenging to build a NDI – EWT model that can accurately represent all levels of senescence

in the plot. Another source of errors was the wind effect. Although the plot was scanned in non-

windy conditions on both dates, there was a gentle breeze in October during the scan. This may

have reduced the accuracy of aligning the point clouds from the two instruments in the October

dataset, leading to higher errors in estimating EWT on a point-by-point basis. However, this

was not obvious in the RMSE of point cloud alignment, which was 3 mm in both datasets.

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6.5.3 Detecting the temporal changes in EWT

Table 6-8 shows the change in EWT between August and October for the four trees used in

validating the EWT estimates. The destructive sampling results showed an increase in EWT for

the Swedish whitebeam, ash and beech trees. The highest increase in EWT was observed in the

beech tree, whilst the ash tree showed the least change in EWT between the two dates. On the

other hand, some decrease in EWT was observed in the holly tree. However, the change in EWT

was measured only for the sections of the trees from which leaf samples for validation were

collected and did not necessarily represent the change in EWT for the whole canopy, which

would require collecting leaf samples from all canopy layers (see Section 6.5.4 for EWT vertical

profiles).

Using the pooled EWT estimation model to detect the change in EWT resulted in an

overestimation of the increase in EWT for the Swedish whitebeam, indicating that EWT

increased by 26.7 % in this section of the tree, while leaf sampling showed that it only increased

by 17.4 %, with 9.3 % difference between them. On the other hand, it underestimated the

increase in EWT for the beech tree, which had an increase in EWT by 20.6 % according to leaf

sampling, and by 15.8 % according to TLS (4.8 % difference). The change in EWT was detected

more accurately for the ash and holly trees as shown in Table 6-8, with the difference between

actual and estimated EWT change (%) being 1.2 % and 1.6 % respectively.

Using the species-specific EWT estimation models also resulted in an overestimation of the

difference between EWT in August and October for the Swedish whitebeam, ash and beech

trees, and underestimation for the holly tree. The overestimation was very significant in the

beech tree. Overall, the pooled EWT model detected the change in EWT more accurately than

the species-specific models. Table 6-8 summarizes the observed temporal change in EWT and

the accuracy of detecting such change using TLS data.

The accuracy of detecting the change in EWT was mainly a function of the EWT estimation

errors on both dates. An overestimation or underestimation of EWT in both dates, with similar

magnitude of errors, produced the most accurate estimation of the change in EWT. A higher

magnitude of error in one dataset than in the other produced less accurate estimates of the

change in EWT, while overestimating EWT in one dataset and underestimating it in the other

produced the least accuracy, as observed in the Swedish whitebeam tree. Overall, the direction

and magnitude of the change in EWT was successfully characterized using TLS in the four

sampled trees, showing the potential of this method to be used in detecting the impact of drought

111

on vegetation. With the very high temporal resolution of TLS, being independent on solar

illumination or limited by cloud coverage, it can be used to fill the gaps in time series produced

using optical RS spaceborne sensors.

Table 6-8. Temporal changes in EWT between August and October.

Swedish

Whitebeam Ash Beech Holly

Actual EWT

(g/cm2)

August 0.0092 0.0127 0.0068 0.0266

October 0.0108 0.0139 0.0082 0.0239

Difference 0.0016 0.0012 0.0014 -0.0027

Difference (%) 17.4% 9.4% 20.6% -10.2%

Estimated EWT

(g/cm2)

species-specific

August 0.0084 0.0122 0.0058 0.0268

October 0.0106 0.0138 0.0089 0.0245

Difference 0.0022 0.0016 0.0031 -0.0023

Difference (%) 26.2% 13.1% 53.4% -8.6%

Estimated EWT

(g/cm2)

pooled model 2

August 0.0091 0.0121 0.0076 ---

October 0.0114 0.0131 0.0088 ---

Difference 0.0023 0.001 0.0012 ---

Difference (%) 25.3% 8.3% 15.8% ---

The temporal change in EWT was also observed in the visual inspection of the point clouds of

the trees on both dates, as shown in Figure 6-17 for Swedish Whitebeam tree 1 as an example.

Figure 6-17. EWT pointcloud of Swedish Whitebeam tree 1 in August (left) and in October

(right). An increase in EWT was observed in October.

6.5.4 Vertical profiles of EWT

Figure 6-18 shows the vertical profiles of EWT for six trees in the plot: two ash trees, two

Swedish whitebeam trees, and two holly trees. The sycamore tree was excluded following the

(g/cm2)

112

severe errors in EWT estimation. The beech tree was partially occluded by an ash tree and a

holly tree, resulting in few laser beam returns from the middle and upper canopy, and thus it

was not possible to generate a vertical profile of EWT.

Figure 6-18. EWT vertical profiles in August and October: (a) Ash tree 1, (b) Ash tree 2, (c)

Swedish Whitebeam tree 1, (d) Swedish Whitebeam tree 2, (e) Holly tree 1 and (f) Holly tree 2.

Both ash trees, in August and October, had higher EWT in the canopy top than in the canopy

bottom, agreeing with the findings of the indoor dry-down experiment (Chapter 4) and the forest

dataset (Chapter 5). For ash tree 1, EWT was 29% higher in the canopy top than in the canopy

bottom in August, and 34% higher in the canopy top than in the canopy bottom in October,

whilst for ash tree 2, EWT was 67% higher in the canopy top in August and 34% higher in the

canopy top in October. Both trees had higher EWT in October than in August in all layers,

suggesting that when the trees were stressed in August, they lost EWT from all layers, agreeing

with the findings of the indoor dry-down experiment in which the deciduous snake-bark maple

tree was losing EWT from all layers while being dried down over a period of eight days

(Chapter 4). Additionally, the vertical profiles showed that ash tree 2 had lower EWT than ash

tree 1 and also was more stressed during the heatwave in August, losing more EWT than ash

tree 1, especially in the canopy bottom.

(a) (b) (c) (d)

(e) (f)

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The Swedish whitebeam trees showed different behaviour to the ash trees. Both trees had higher

EWT in the canopy top than in the canopy bottom in both dates, except for tree 1 in August in

which canopy layers between 7 m and 10 m had the lowest EWT. However, the vertical profiles

of EWT were hourglass shaped with the lowest EWT being in middle canopy layers. For tree 1,

EWT was higher in October than in August and comparing the EWT vertical profiles in both

dates revealed that there was almost no change in EWT in middle canopy layers, while EWT

increased in the top and bottom layers in October. In August, EWT was higher in the canopy

top than in the canopy bottom by only 4%, while it was higher by 27% in October. For tree 2,

slight change in EWT was detected between August and October, with EWT in October being

slightly higher. EWT was higher in the canopy top than in the canopy bottom by 48% and 49%

in August and October respectively. The tree either was not affected by the heatwave, or had

not yet recovered from the stress caused by the drought, considering that overall it had lower

EWT in October than tree 1.

Similar to the Swedish whitebeam trees, holly tree 1 had hourglass EWT vertical profiles in

August and October, with EWT in middle layers being less than that in the top and bottom of

canopy. However, contrary to the behaviour observed in Swedish whitebeam tree 1, holly tree 1

maintained the same EWT in the top and canopy bottom during and after the heatwave, while

middle canopy layers showed an increase in EWT in October. However, holly tree 2 had

different EWT vertical profiles than holly tree 1 and also than all the other species. EWT was

lower in the canopy top than in the canopy bottom in both August and October by 17% and 4%

respectively. Also, there was a slight change in EWT between the two dates in the canopy

bottom, while EWT increased in the canopy top in October.

It was unclear why two trees of the same species, in the same plot, had substantially different

vertical profiles of EWT and also reacted in a different manner to stress during a heatwave. A

possible explanation is the illumination conditions of the plot, which may have caused the two

trees to be illuminated in different ways, thus resulting in the trees growing sun/shade leaves in

different canopy layers. Typically, sun leaves grow in the canopy top because the top layers of

the canopy receive the majority of irradiance (Chazdon and Fetcher, 1984). However, as there

was a wide gap in the canopy in the middle of the plot, sun leaves were not necessarily in the

canopy top only, depending on how the trees were illuminated. Holly is known to be able to

grow different types of leaves depending on the position of leaves in the canopy, with shaded,

canopy bottom leaves having sharp prickles to protect them from animals and insects, while

sun leaves are smaller and smoother (Herrera and Bazaga, 2013). However, despite all leaf

114

samples being collected from the canopy bottom in both dates, among the eighteen leaf samples

collected only four were prickly, while fourteen were smooth, suggesting that the canopy

bottom layers were sun leaves. This can explain why the two holy trees in the plot had high

EWT in canopy bottom layers, and also attempted to maintain the EWT in these layers

unchanged when the trees were stressed, while losing moisture from middle canopy layers in

holly tree 1, and from the canopy top in holly tree 2.

6.6 Species- and site-independent EWT estimation model

The leaf level results discussed in section 6.5.1 showed that it was not possible to fit a general,

site-independent EWT estimation model for senescent leaves, if they were at different levels of

senescence. However, it was possible to fit a pooled, species- and site-independent EWT model

for green leaves, described in Equation (6.20), combining the leaf samples collected in the

August dataset with the leaf samples collected in the indoor dry-down experiment (Chapter 4).

To further investigate this finding, the leaf samples collected in the forest dataset (Chapter 5)

were added to the model (Figure 6-19), resulting in a very high fitting accuracy (R2 = 0.91).

The model can be described as:

EWT (g cm-2) = 0.0543 × NDI – 0.0034 (6.22)

Figure 6-19. The general, species- and site-independent pooled NDI – EWT model that

combined all leaf samples.

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The all-samples pooled EWT model further showed that the NDI – EWT relationship was not

driven by species as much as it was driven by the variation in leaf internal structure. Variation

in NDI values of leaves with similar EWT was in some cases more significant within individual

species than it was between different species, as observed in the beech and lime leaf samples.

Another example was the sycamore and oak leaf samples, as at similar EWT values, NDI of

some sycamore leaves was more similar to that of oak leaves than to other sycamore leaves.

The same was observed in ash leaf samples. The high fitting accuracy of the model suggested

that such variation in NDI was insignificant; however, as discussed in Section 6.5.1, a high

fitting accuracy of the EWT estimation model at the leaf level does not necessarily reflect the

accuracy of the model at the canopy level. Thus, the all-samples pooled model was tested at the

canopy level in both forest and August datasets. Errors in the EWT estimations are shown in

Table 6-9 for the August dataset and Table 6-10 for the forest dataset.

Table 6-9. For August dataset, a comparison between the errors observed in the EWT estimation

using the all-samples pooled EWT model and the Park-only EWT model.


Species All samples EWT model Park only EWT model

Swedish Whitebeam 5.3% -2.2%

Ash -1.8% -4%

Beech 25% 12%

Table 6-10. For forest dataset, a comparison between the errors observed in the EWT estimation

using the all-samples pooled EWT model and the forest-only EWT model.


All samples EWT model Forest only EWT model

Tree Species Canopy top Canopy bottom Canopy top Canopy bottom

1 Sycamore -9.1% 3% -5.3% 7.2%

2 Sycamore -16% 0.2% -13.5% 4.3%

3 Sycamore -6.6% 0.3% -2.7% 4.3%

4 Sycamore --- 3.2% --- 7.3%

5 Beech -6.3% --- -2.8% ---

6 Sycamore -4.9% --- -1% ---

8 Oak --- 5.8% --- 10.2%

9 Oak 2.3% --- 6.7% ---

10 Oak -2.1% --- 2.1% ---

11 Oak -6.3% --- -2.4% ---

12 Sycamore -16% --- -13.3% ---

13 Oak -9.1% --- -5.4% ---

Average error 7.9% 2.5% 5.5% 6.7%

Total error 6.6% 6.3%

116

For the August dataset, the EWT estimation accuracy obtained using the all-samples pooled

EWT model was comparable to the park-only pooled EWT model, except for the beech tree.

The overestimation of the beech tree EWT indicated that the tree had leaves significantly

thinner, or with different mesophyll structure, than the leaves of the other trees in the plot.

Although leaf thickness was not measured, LMA measurements showed that beech leaf samples

had lower LMA than ash and Swedish Whitebeam leaves (0.0056, 0.0071 and 0.0075 g/cm2,

respectively), suggesting, but not confirming, that they were thinner.

For the forest dataset, the average of the relative errors in the EWT estimation using the all-

samples pooled model was 6.6%, insignificantly higher than the error observed when the forest-

only pooled model was used (6.3%). However, many of the errors in the canopy top layer

increased and the errors in the canopy bottom layers decreased, as a result of the EWT

estimation model containing many canopy-bottom leaves. Error in each individual tree was <

10 %, except for sycamore trees 2 and 12, for which EWT was underestimated. The high errors

and the EWT underestimation in the two trees indicated that their leaves were thicker, which

was reflected in their higher LMA in comparison to the leaves from the other sycamore trees

(0.0029 g/cm2 for sycamore trees 2 and 12 and average of 0.0021 g/cm2 for the remaining

Sycamore tree, ranging between 0.0017 and 0.0024 g/cm2). The results revealed that the all-

samples pooled EWT model was species-independent, but site-independent only to an extent.

6.7 3D mapping of FMC

Typically, FMC and EWT are correlated water status metrics, as FMC can be quantified as

EWT divided by LMA. FMC is linked to the potential of fire ignition and propagation (Viegas

et al., 1992), in addition to the fire spread rate (Nelson Jr, 2001). Thus, it has been widely used

in wildfire modelling and early detection of wildfire risk (Danson and Bowyer, 2004).

Following the successful estimation of EWT in 3D using NDI, FMC of leaf samples collected

in the October dataset was measured, following Equation (6.23), and was directly linked to

NDI. NDI – FMC relationships were established at the leaf level and applied at the canopy

level to investigate the possibility of generating 3D FMC point clouds and FMC vertical

profiles. Figure 6-20 shows the NDI – FMC relationships established at the leaf level.

𝐹𝑀𝐶 (%) = (𝐹𝑊 − 𝐷𝑊

𝐷𝑊) × 100 (6.23)

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Figure 6-20. The NDI – FMC relationships at leaf level for October dataset leaf samples.

The highest FMC was observed in ash leaf samples (189 %), followed by holly leaf samples

(168 %). The beech leaf samples had a lower FMC (138 %), whilst FMC observed in the

Swedish whitebeam samples was the lowest (126 %). This differed from the observation

obtained for EWT of the same leaf samples, which showed that holly had the highest EWT and

beech had the lowest. Moderate linear correlation was observed between NDI and FMC for all

species (R2 = 0.53, 0.51, 0.60 and 0.45 for Swedish whitebeam, beech, ash and holly,

respectively), and the correlation was lower than that achieved in the NDI – EWT relationships.

This was expected, as EWT is known to be more correlated to reflectance in the optical domain

than FMC (Ceccato et al., 2001). NDI and FMC were found to be directly proportional for

Swedish whitebeam and holly species, but inversely proportional for ash and beech (Figure

6-20). The NDI – FMC relationship was found to be species-dependent and it was not possible

to fit a pooled, species-independent FMC estimation model. This observed variation in the NDI

– FMC relationships between species was caused by the difference in LMA between them, as

FMC is sensitive to the change in LMA (Riaño et al., 2005; Yebra et al., 2008). This agreed

with the findings of Ceccato et al. (2001), reporting that EWT and FMC were not always

directly related, as they are two different ways to define vegetation water content, and observing

an inverse relationship between them in some species, as a result of the LMA effects.

The species-specific NDI – FMC models can be described as follows:

FMC (%) = 581.46 × NDI + 4.58, for Swedish whitebeam (6.24)

FMC (%) = -445.18 × NDI + 216.27, for beech (6.25)

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FMC (%) = -580.57 × NDI + 342.55, for ash (6.26)

FMC (%) = 83.86 × NDI + 137.86, for holly (6.27)

At the canopy level, FMC was estimated with errors < 8 % in the four trees used for validation,

with the average error in the estimation being 4.5 % (Table 6-11). 3D FMC point clouds were

generated for the same six trees shown in Figure 6-18. Figure 6-21 shows the 3D FMC point

cloud of Swedish whitebeam tree 1. Similar to the 3D EWT point clouds, the FMC point clouds

revealed a difference between leaf and wood, and also showed vertical heterogeneity in FMC

distribution within canopy.

Table 6-11. The errors in the FMC estimations at canopy level.

Species Actual FMC (%) Estimated FMC (%) Relative error (%)

Swedish whitebeam 146.8 135.5 -7.7

Ash 189.7 186.4 -1.6

Beech 137.5 128.6 -6.4

Holly 166.1 169.8 2.2

Figure 6-21. 3D FMC point cloud of Swedish whitebeam tree 1.

FMC vertical profile was generated for each tree (Figure 6-22). FMC vertical profiles concurred

with the visual inspection of point clouds and revealed some vertical variation in all trees. The

vertical profiles of FMC varied between species, and also showed some variation between the

two trees from each species. For the Swedish whitebeam trees, the trees displayed hour-glass

shaped FMC distribution, similar to their EWT vertical profiles, with the lowest FMC being

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located in the middle of the canopy, which was more obvious in tree 1 than in tree 2. The two

trees had similar FMC in the upper canopy (layers > 6 m), whilst tree 1 had a higher FMC in

the lower canopy than tree 2. Overall, tree 1 had 28 % higher average FMC than tree 2 (138 %

and 108 %, respectively).

Figure 6-22. FMC vertical profile for six trees in the plot: (a) Swedish whitebeam trees 1 and 2,

(b) ash trees 1 and 2, and (c) holly trees 1 and 2.

For ash trees, the two trees had similar FMC in the upper canopy (layers > 7 m), while tree 2

had higher FMC in the lower canopy than tree 1. However, the difference in the mean FMC

between the trees was less significant than that in the Swedish whitebeam trees, as tree 2 had

only approximately 7 % higher FMC than tree 1. Also, the highest observed FMC in ash trees

was in the canopy bottom, while the lowest was in the upper canopy, showing different FMC

vertical profiles than the hour-glass shaped FMC vertical profiles observed in the Swedish

whitebeam trees. The FMC vertical profiles were also the opposite to the EWT vertical profiles,

as a result of NDI being inversely proportional to FMC for ash. Holly trees showed the least

vertical variation in FMC. Tree 1 had an hour-glass shaped FMC vertical profile, with FMC in

the middle canopy being slightly lower than that in the upper and lower canopy. However, tree

2 showed a different behaviour, as FMC in the upper and middle canopy was almost constant,

while FMC was slightly higher in the lower canopy. The FMC vertical profiles matched the

EWT vertical profiles.

Although the results obtained from this dataset were promising, more experiments that include

leaf samples for validation from multiple canopy layers are needed to validate the accuracy of

the 3D FMC estimates. The method also needs to be tested in a real forest environment, and the

data collected in Wytham dataset (Chapter 5) was insufficient for this, as LMA measurements

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were carried out in the upper and lower canopy only, while LMA vertical profiles are needed

to fully validate this approach. Combining TLS data with multispectral or hyperspectral

imagery can lead to better quantifying of the LMA vertical heterogeneity in forest canopies,

and thus allowing the use of TLS to provide 3D FMC point clouds in forest stands. Overall,

FMC was outside the scope of this research, which focuses mainly on EWT, and this dataset

can only be considered a proof-of-concept for future work.

6.8 Summary

This chapter investigated the transferability of the EWT estimation approach to two different

sites: willow bioenergy crop plots and a mixed-species tree plot. The experiments described in

this chapter aimed at studying the effects of wind and senescence of leaves on the accuracy of

the EWT estimation, and investigating the possibility of using the proposed EWT estimation

approach to detect temporal changes in EWT.

Regarding the wind effects, as expected, it was found that in windy conditions mapping EWT

in 3D was not possible because of the low registration accuracy of the point clouds from the

two TLS instruments. However, it was still possible to estimate the average EWT in the whole

canopy or in individual layers of the canopy, as long as calculations were not done on a point-

by-point basis.

Leaf senescence was found to significantly affect the NDI – EWT relationship, as it alters the

leaf internal structure in a way that seemed to vary depending on the leaf level of senescence.

It was not possible to fit a pooled, site-independent NDI – EWT model. However, it was

possible to fit a pooled, site-specific EWT estimation model for leaf samples that were at similar

levels of senescence. On the other hand, for green leaves, a species-, site-independent EWT

estimation model was derived, and was found to be able to estimate EWT with accuracy very

close to that obtained using site-specific models. However, care must be taken when applying

the model, as large errors in EWT estimation would be obtained in trees that have leaves with

significantly different internal structure (thinner/thicker) than the majority of leaves used in

building the model.

It was possible to detect the temporal change in EWT using TLS data. The accuracy depended

mainly on the errors obtained in EWT estimation in the different dates. Consistent errors in both

dates resulted in high accuracy in detecting the change in EWT. However, less accuracy was

obtained when EWT was overestimated in one dataset and underestimated in the other.

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Although the results obtained were very promising, the method needs to be tested in a real forest

environment to investigate the ability of TLS data to detect the change in EWT in such a

challenging environment.

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Chapter 7. Integrating the 3D EWT estimates in radiative transfer

modelling

7.1 Introduction

The results obtained in Wytham Woods (Chapter 5) revealed vertical heterogeneity in EWT in

the forest plot, with the upper canopy having an average of 24% higher EWT than the lower

canopy. As discussed in Chapter 2, the effects of such vertical heterogeneity on the accuracy of

EWT estimation using optical remote sensing data is often ignored, as it is difficult to measure

and to account for in EWT estimation models. This chapter is dedicated to integrating the 3D

EWT estimates obtained in Chapter 5 into the 3D RTM DART. Two groups of simulations

were conducted to study the effects of woody materials, understory, and EWT vertical

heterogeneity on the Bottom of the Atmosphere (BOA) reflectance and the NDWI, using two

different forest scenes. Additionally, example scenarios of water stress, that affected the whole

canopy or started in the lower canopy then spread upward, were simulated to investigate which

canopy layers dominated the forest plot reflectance. Such water stress can be caused by drought

or infections by pests or diseases that affect the lower canopy first, such as oak anthracnose,

oak leaf blister, sycamore anthracnose, and ash dieback (Downer, 2006; Horst and Horst, 2013;

Pokorny, 2015). To link the simulations to real satellite data, the simulated reflectance and

NDWI were compared to actual values retrieved from Sentinel-2A near- and shortwave-

infrared bands (864 nm and 1613 nm, respectively).

7.2 DART concept and background

DART (Gastellu-Etchegorry et al., 1996) is a 3D RTM which has been developed in the

CESBIO (CEntre for the Study of the BIOsphere from space) laboratory since 1992. DART is

capable of simulating the radiative budget in any user-defined Earth-atmosphere system, as

acquired by airborne and spaceborne optical sensors, in the visible and infrared (near,

shortwave, and thermal) regions of the electromagnetic spectrum (Gastellu-Etchegorry et al.,

2004). Radiative budget refers to the balance between solar radiation entering an Earth-

atmosphere system and reflected radiation and thermal emissions from the system (Kiehl and

Trenberth, 1997). DART computes the radiation propagation through the system, simulating

the radiation scattering and absorption by the atmosphere and Earth landscape, plus thermal

emissions from both of them, and produces a 3D radiative budget, in addition to images at

various altitudes. Simulated images are produced at BOA and Top of the Atmosphere (TOA)

for any user-defined sensor, viewing angles, sun illumination angles, and spectral bands, in

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addition to images at any other sensor altitude specified by the user. Simulated images are

coupled with Look Up Tables (LUTs) that contain BOA and TOA spectral products, including

scene radiance, irradiance, albedo, and reflectance, for all specified sensors and configurations.

Irradiance refers to the incident radiation, radiance is the reflected radiation, albedo is the ratio

between radiance and irradiance, while reflectance is the same fraction for a single incidence

angle (sun position) (Wanner et al., 1997). TOA products resemble satellite sensor

measurements and are influenced by atmospheric effects (scattering and absorption), and thus

require radiometric corrections in real-life scenarios. Among the aforementioned DART

products, BOA reflectance was the focus of this study, as it is the product being used in deriving

vegetation indices. The numerous other spectral products that DART provides are described in

detail in Gastellu-Etchegorry et al. (2015).

DART simulation is conducted using four modules: Direction, Phase, Maket, and Dart. The

inputs for the Direction module are the sensor characteristics, viewing angle, spectral bands,

and sun angular position (azimuth and zenith angles). The module then computes discrete

directions of light propagation to be used in flux tracking during the simulation. For the Phase

module, the atmospheric parameters need to be defined, including gas and aerosol (solid or

liquid particles suspended in the air) density profiles, which can be defined manually, imported

into the model, or selected from the model database. In addition, the optical properties of all

objects in the scene need to be specified. Optical properties can be manually defined or selected

from the DART database. The Phase module uses the inputs to calculate scattering phase

functions for elements in the Earth landscape and the atmosphere, based on their pre-defined

optical properties and geometry. A scattering phase function defines how radiation is scattered

by an element at a given wavelength (Seinfeld and Pandis, 2016).

In the Maket module, urban, natural or mixed 3D Earth scenes, with different levels of

complexity, can be constructed as a set of 3D cells (voxels) that contain the scene elements.

The dimensions of the cells affect the processing time and define the output spatial resolution.

3D objects in the scene can be created in the module or imported into it, then their optical

properties, pre-defined in the Phase module, are assigned. 3D objects consist of turbid cells,

facets (triangles), or a combination of both, depending on the type of the object. For example,

tree trunks and urban features are constructed as facets, while water and vegetation bodies

(grass, crop fields, and foliage) are approximated as turbid mediums. A vegetation turbid

medium is a set of infinitely small flat surfaces, defined by their leaf angle distribution, volume

density, and optical properties. Vegetation foliage can also be modelled as facets, defined by

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their orientation in space and surface area. In addition to creating the Earth scene and the 3D

objects in it, the atmosphere geometry needs to be specified. DART models the atmosphere as

three major sections: Lower Atmosphere (LA), Mid Atmosphere (MA), and High Atmosphere

(HA). LA has the height and cell size of the Earth landscape. MA is modelled as a number of

cells while HA is modelled as a number of layers. The dimensions of the MA cells and HA

layers can be defined by the end-user, or DART default atmosphere geometry (Table 7-1) can

be used, depending on the aims of the simulations.

Finally, the Dart module uses the outputs from the Direction, Phase, and Maket modules, and

computes radiation propagation and interactions through the atmosphere and the Earth scene,

by tracing iteratively radiation fluxes per cell (voxel), and generates the scene 3D radiative

budget, simulated images, and spectral products. Gastellu-Etchegorry et al. (2015) provides a

full description of the DART model, including the physical principles of the model, flux-

tracking approaches, different products, and how to define and simulate terrestrial and airborne

optical and LiDAR sensors. Figure 7-1 shows a representation of a DART Earth-atmosphere

system. The model used in this study was DART 5.6.7.

Table 7-1. DART default atmosphere geometry.

Atmosphere

height MA height MA cell size HA height

HA layer

thickness

84 km 4 km 100 m × 100 m × 500 m 80 km 2 km

Figure 7-1. DART representation of Earth-atmosphere system. Figure is adapted from Gastellu-

Etchegorry et al. (2015).

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Numerous previous studies have successfully coupled DART simulations with airborne and

spaceborne optical data to retrieve vegetation biophysical and biochemical characteristics.

SPOT and Ikonos imagery were used for estimating forest LAI, crown coverage, and leaf

chlorophyll content through inversion of DART (Gascon et al., 2004). Sepulcre-Canto et al.

(2009) used ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

satellite imagery and DART simulations to successfully differentiate between irrigated and

rain-fed olive orchards. Hernández-Clemente et al. (2012) coupled airborne multispectral data

with DART simulations to examine the performance of carotenoid vegetation indices at leaf

and canopy levels in a pine forest. Banskota et al. (2013) estimated forest LAI using AVIRIS

hyperspectral data with high accuracy through inversion of the DART model. Malenovský et

al. (2013) derived a new leaf chlorophyll content vegetation index from the AISA Eagle

airborne imaging spectrometer data using DART simulations. Yáñez-Rausell et al. (2015) used

CHRIS-PROBA images and DART simulations to successfully estimate leaf chlorophyll

content of a Norway spruce stand, validating the estimation using AISA Eagle airborne imagery

of the same stand. Ferreira et al. (2018) combined AISA Eagle airborne imagery with DART

simulations to successfully estimate individual tree crowns’ structural and biochemical traits

(chlorophyll and carotenoid contents) in a diverse tropical forest.

Other applications of the DART model have included designing satellite sensors (Durrieu et

al., 2013), studying canopy heterogeneity in Amazonian forests (Barbier et al., 2010), studying

variation in tropical forest texture and canopy structure (Barbier et al., 2012), estimating

tropical forests biomass (Proisy et al., 2012), and modelling vegetation canopies’ 3D

distribution of photosynthesis rates (Guillevic and Gastellu-Etchegorry, 1999). No apparent

previous studies have modelled the vertical variation in vegetation biochemical characteristics

(including EWT) in 3D RTMs, including DART.

7.3 Parametrizing the model

Two spectral bands were defined and used in all simulations, corresponding to Sentinel-2A

near- and shortwave-infrared bands (bands 8A and 11, respectively). The first band had a central

wavelength of 864 nm and a bandwidth of 33 nm, while the second band had a central

wavelength of 1613 nm and a bandwidth of 143 nm. Although DART produces spectral

products for various sensor viewing angles, the results presented in this study corresponded to

zero zenith and azimuth sensor viewing angles. DART default sun angular position was used

(30º zenith angle and 225º azimuth angle). The number of discrete light propagation directions

and the number of iterations for flux tracking were 100 and 6 respectively, as recommended by

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the developer of the model for the spectral range in the scope of this study. The U.S. standard

atmosphere 1976 model (DART database) was used to parametrize how pressure, temperature

and density of the atmosphere change with altitude, defining the gas and aerosol vertical profiles

and optical properties. As the aim of the simulations was not to study the atmospheric effects

on TOA reflectance, it was not necessary to accurately define the atmosphere, and using a

standard atmosphere model was considered sufficient, as it was kept constant in all simulations.

Optical properties of the understory were predefined as two Lambertian models (DART

database). The first resembled healthy grass (Figure 7-2a), and the second resembled leaf litter

(Figure 7-2b). For the woody materials, bark-deciduous Lambertian model (DART database)

was used (Figure 7-3).

Figure 7-2. Pre-defined understory optical properties: (a) healthy grass, and (b) litter.

Figure 7-3. Pre-defined bark-deciduous optical model used for woody materials.

Leaf optical properties were simulated using the PROSPECT model, implemented in DART,

using the model parameters shown in Table 7-2, unless otherwise stated, and changing EWT

depending on the simulation. The leaf structure coefficient was defined based on the values

retrieved from field spectroscopy measurements conducted in the forest, as reported in Calders

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et al. (2018). LMA was defined based on the destructive sampling conducted in the forest

(Chapter 5). Methods to implement the vertical variation of EWT in the model are presented in

Section 7.4. It is worth mentioning that leaves were modelled as objects with double-face

optical properties. That is, light rays were allowed to scatter on the front face, transmit through

leaves, and scatter again on the back face. On the other hand, woody materials were modelled

as objects with single-face optical properties, meaning that light rays only scattered on the outer

surface of objects.

Table 7-2. PROSPECT parameters used to simulate leaf optical properties.

Leaf structure

coefficient

Chlorophyll

(µg cm-2)

Carotenoid

(µg cm-2)

Brown

pigment

LMA

(g cm-2)

EWT

(g cm-2)

1.5 47.7 4.4 0.0 0.0028 variable

Two forest scenes (Sections 7.4 and 7.5) were constructed, having the dimensions of 35 m ×

45 m and 40 m × 40 m respectively, and average heights of 16.5 m and 27.6 m respectively.

Default values for the scene cell (voxel) dimensions were used (0.3 m × 0.3 m × 0.3 m). DART

default atmosphere geometry (Table 7-1) was utilized, keeping the atmosphere optical and

geometric characteristics constant in all simulations.

7.4 Forest scene 1

Forest scene 1 represented a mixed-species (sycamore and oak) forest plot, based on the TLS

data obtained in the data collection campaign conducted in Wytham Woods (Chapter 5). All

the processes described in this section were conducted in CloudCompare v. 2.6.2 software,

unless otherwise stated. To build the forest scene, tree stem locations were determined from the

TLS point cloud. First, a slice was taken slightly above ground in the plot point cloud, which

contained the ground and approximately 20 cm of the stems. Then, the approximate coordinates

of the centre of each stem at ground level were acquired and considered as the tree location. As

the aim of the planned simulations was to investigate the effects of EWT vertical heterogeneity

on BOA reflectance and NDWI, and not to investigate the effects of tree structure variation, it

was not necessary to model all the trees in the plot. Instead, to reduce computation time, one

sycamore tree and one oak tree were chosen from the plot TLS point cloud to be used in

reconstructing the forest scene. The selection of the trees and generation of 3D tree models were

performed as follows:

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1) The point clouds of the twelve sampled trees around the canopy walkway (Figure 5-2 and

Table 5-1) were visually inspected, and six trees (four sycamores and two oaks), which visually

suffered the least occlusion, were selected.

2) Leaf and wood in each tree were already separated for EWT estimation (Section 5.4.2). The

leaf point cloud was also already divided into multiple layers to generate EWT vertical profiles

(Section 5.5.4). A Poisson surface reconstruction model, which generates a triangle mesh from

a set of oriented 3D points, using the algorithm described in Kazhdan and Hoppe (2013), was

used to reconstruct 3D objects (meshes) from leaf layers and wood point clouds of each tree.

According to the developer of the model, the model is recommended for application on closed

3D shapes, thus applying it to leaf and wood point clouds resulted in highly distorted 3D objects,

as the model attempted to produce closed shapes, disregarding the shape of leaves and tree

branches (Figure 7-4b and Figure 7-5b). However, the model generated the density histogram

of the points involved in creating each triangle in the mesh. The density histogram was used to

clean the 3D objects and remove the triangles that had very low density by choosing a threshold,

based on visual inspection of the meshes and original point clouds. Figure 7-4 shows an

example of generating a 3D object from the TLS point cloud for Sycamore tree 1 woody

materials. Figure 7-5 shows the same for a single leaf layer in the same tree.

3) The average number of triangles in each tree 3D object was 41 million, as a result of the very

dense point clouds used to generate the 3D models. Using 3D objects that had such a high

number of triangles was not practical, because of software and hardware limitations, which

required reducing the number of triangles. For this, each tree 3D object was imported into

MATLAB and the standard MATLAB function ‘reducepatch’ was applied to reduce the number

of triangles. The function maintained the boundaries of each shape, attempting to preserve the

surface area, and merged the triangles inside the shape to reduce their number. The input was a

reduction coefficient (percentage). This was decided by trial and error, by measuring the total

mesh surface area before and after applying different reduction coefficients. It was found that

the module was able to reduce the number of triangles by approximately 96% with only a slight

reduction in the mesh surface area. The average number of triangles in the tree objects after

reduction was approximately 1.3 million. This number was still considered high. However,

using a higher reduction coefficient was not possible, as it was found to have more impact on

the surface area.

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Figure 7-4. Creating 3D object for Sycamore tree 1 woody materials: (a) wood point cloud, (b)


in mesh triangles, and (c) the final 3D object after removing the noise using the density

histogram, guided by visual inspection of the original point cloud.

4) Leaf layer 3D objects of each tree were used to estimate individual tree LAI. First, the total

one-sided surface area of the triangles in each layer was calculated. Afterwards, to calculate the

canopy projected area, a polygon was interactively fit to the canopy outer perimeter by

manually drawing a polyline. The polygon was then projected on a horizontal plane using

AutoCAD 2016 and the area inside the polygon was calculated and considered the canopy

projected area. LAI of the canopy was calculated as the summation of the one-sided surface

area of triangles divided by the canopy projected area (Table 7-3). Sycamore tree 1 and oak

tree 10 (Figure 7-6) were chosen to be used in DART simulations as they had the closest LAI

to the global average LAI for deciduous forests, which ranges between 3.9 and 5.1, as reported

in Asner et al. (2003), and in the region of 3-6, as reported in Kozlowski and Pallardy (1996).

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Figure 7-5. Creating 3D object for a leaf layer in Sycamore tree 1: (a) layer point cloud, (b)


in mesh triangles, and (c) the final 3D object after removing the noise.

Table 7-3. LAI estimated from the trees 3D models.

Tree label Species Height (m) LAI

1 Sycamore 15.80 3.42

2 Sycamore 15.40 2.62

6 Sycamore 17.25 3.01

12 Sycamore 17.42 2.31

10 Oak 16.49 3.03

11 Oak 17.90 1.85

5) To import the trees into DART, 3D objects of each tree (leaf layers and wood) were combined

into a single group of objects that resembled the whole tree. The species and coordinates of

each tree in the plot were used to distribute the 3D tree objects in the scene. In each location

that corresponded to a sycamore tree, the 3D model of sycamore tree 1 was placed, and in each

location that corresponded to an oak tree, the 3D model of oak tree 10 was placed. DART

considered each tree object a single object that consisted of multiple groups (leaf layers and

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wood), making it possible to assign specific optical properties to each layer in the tree to model

the vertical variation of EWT. For this, an average EWT vertical profile of the sycamore trees

in the plot (Table 7-4) and an average EWT vertical profile of the oak trees in the plot (Table

7-4) were generated from the EWT vertical profiles retrieved in Chapter 5. For each canopy

layer, a PROSPECT simulation was conducted, using the parameters shown in Table 7-2, and

EWT of the layer, and the resulting optical model was assigned to the layer. Figure 7-7 shows

a 3D view of the forest scene.

Figure 7-6. The 3D tree objects used to build forest scene 1: (a) Sycamore tree 1, and (b) oak

tree 10.

Table 7-4. The EWT vertical profiles for the 3D models of the Sycamore and Oak trees, used

to build forest scene 1. Height was measured to the centre of each layer.

Sycamore Oak

Layer Height (m) EWT (g cm-2) Layer Height (m) EWT (g cm-2)

1 15.1 0.0130 1 15.62 0.0131

2 13.9 0.0125 2 14.25 0.0130

3 12.9 0.0122 3 13.25 0.0128

4 11.9 0.0121 4 12.25 0.0122

5 10.9 0.0119 5 11.25 0.0120

6 9.9 0.0115 6 10.25 0.0119

7 8.9 0.0108 7 9.25 0.0114

8 7.9 0.0105 8 7.75 0.0106

9 6.9 0.0107 N/A N/A N/A

10 5.9 0.0106 N/A N/A N/A

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Figure 7-7. 3D view of forest scene 1.

7.5 Forest scene 2

Forest scene 2 was a 40 m × 40 m subset of the 3D model of a one hectare forest stand in

Wytham Woods, Oxford, UK, described in detail in Calders et al. (2018). The one hectare 3D

model was reconstructed using data collected with a RIEGL VZ-400 TLS instrument in leaf-on

and leaf-off conditions (Calders et al., 2016). Reconstructing the 3D model is described

thoroughly in Calders et al. (2018), and followed three main steps: (1) tree segmentation, (2)

tree structure modelling, and (3) leaf insertion. Tree segmentation was done using the treeseg

open-source software (Burt et al., 2018), using the leaf-off point cloud. Tree structure modelling

was done by converting each extracted individual tree point cloud to a Quantitative Structure

Model (QSM), which is a geometrical model that describes the complete tree woody

components in an hierarchical order, including the higher order branches (Hackenberg et al.,

2015). For this, cylinders were fitted to the tree point clouds, according to the skeleton of each

tree, following the procedure described in Calders et al. (2015). Leaves were then added to each

tree QSM using the Foliage and Needles Naïve Insertion (FaNNI) algorithm (Åkerblom et al.,

2018), by defining the leaf shape, target leaf area, leaf area density distribution, leaf size

distribution, and leaf orientation distribution, using the leaf-on point cloud as a guidance, as

described in Calders et al. (2018).

The subset used in this research contained 44 trees from three different species: sycamore (Acer

pseudoplatanus), ash (Fraxinus excelsior), and hazel (Corylus avellana). To reconstruct the

forest scene in DART, each tree leaf point cloud was divided into a number of layers, each 1 m

deep, depending on the canopy height. For the woody materials, each tree QSM defined the

woody components as cylinders, with each cylinder defined as two vertices, top and bottom

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respectively, and a radius. As DART is not capable of reconstructing 3D models from such data

type, the cylinders were reconstructed in MATLAB, using the free add-on ‘cylinder

between two points’ function. The outcome was one or more cylinder meshes for each wood

component in each tree, which were then merged together to build the tree woody materials 3D

models (Figure 7-8a). The woody materials 3D models and the leaf layers 3D models were then

grouped and imported into DART to build the forest scene (Figure 7-8b).

Figure 7-8. 3D view of forest scene 2: (a) woody materials 3D objects and (b) plot 3D objects,

combining wood and leaf 3D models.

The forest scene was a realistic representation of the original forest plot, in terms of the tree

species, structure, and locations, unlike forest scene 1, which was reconstructed using only two

tree models representing two species. Plus, as the woody materials were modelled as QSMs

derived from leaf-off TLS data, the small branches and twigs in the upper canopy were

accounted for, whilst they were absent in the tree models used to build forest scene 1 as a result

of occlusion in the TLS leaf-on data. In addition, the average height of the scene was 27.6 m,

being higher than that of forest scene 1 (16.5 m). This variation in tree structure and species

between the two forest scenes can be useful to investigate its effect on the plot reflectance and

the NDWI. As no EWT measurements were conducted in this forest plot, and no EWT vertical

profiles were generated, EWT was assumed to equal 0.014 g cm-2 in all canopy layers, based

on EWT retrieved from destructive sampling of sycamore and ash trees (Chapter 5). EWT was

then modified to simulate different case scenarios, as discussed in Section 7.6. For each canopy

layer, a PROSPECT simulation was conducted using the parameters shown in Table 7-2, except

for LMA that was changed to 0.0022 g cm-2 in this plot, based on the field spectroscopy

conducted in this part of the forest (Calders et al., 2018). Assigning optical properties to each

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canopy layer in the 44 trees was time consuming. This further showed the challenges associated

with modelling the vertical variation in vegetation biochemistry traits.

7.6 Simulations

Two groups of simulations were carried out. The first group aimed at studying how foliage,

woody materials, and understory contributed to the forest plot BOA reflectance and NDWI

(Table 7-5). In forest scene 1, EWT was kept constant at 0.013 g cm-2 in all canopy layers,

being the average EWT of canopy top observed in leaf destructive sampling, while it was kept

constant at 0.014 g cm-2 in all canopy layers of forest scene 2. Sim 1.1 aimed at measuring the

BOA reflectance and NDWI resulting from foliage only, and thus zero reflectance Lambertian

optical models were assigned to the woody materials and understory. Sim 1.2 and 1.3 measured

the contribution of woody materials and understory, respectively, to the plot BOA reflectance

and NDWI, while Sim 1.4 combined both contributions. The results of the simulations were

compared to Sim 1.1 results and the change in reflectance and NDWI was calculated. Sim 1.5

investigated how changing the understory from healthy grass to dry litter would affect the plot

reflectance and cause a change in NDWI. Thus, the results of Sim 1.5 were compared to the

results of Sim 1.4 to calculate the change in reflectance and NDWI.

Table 7-5. Group 1 simulations. EWT value was 0.014 g cm-2 for forest scene 2.

Sim 1.1 Sim 1.2 Sim 1.3 Sim 1.4 Sim 1.5

Foliage PROSPECT

reflectance model

EWT value (g cm-2)

0.013 0.013 0.013 0.013 0.013

Understory reflectance

model Zero Zero

Healthy

grass

Healthy

grass Litter

Woody materials

reflectance model Zero

Bark

deciduous Zero

Bark

deciduous

Bark

deciduous

The predefined optical models: healthy grass (Figure 7-2a), litter (Figure 7-2b), and bark-

deciduous (Figure 7-3), were used in the simulations, as shown in Table 7-5. It is worth

mentioning that assigning zero reflectance optical models to ground and/or woody materials

meant that light rays that would reach the ground or wood components would be terminated. In

a real life scenario, part of the light rays reflected from the ground and wood components can

be scattered again by foliage, and thus add to the canopy reflectance. So, it was expected that

the results obtained in simulations that included assigning zero reflectance optical properties to

ground and/or woody materials would contain a bias.

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The second group of simulations aimed at studying the effect of the EWT vertical heterogeneity

on the BOA reflectance and the NDWI, and to determine which canopy layers dominated the

plot reflectance (Table 7-6). Bark-deciduous Lambertian model was assigned to the woody

materials, and healthy grass was assigned to the understory, except for Sim 2.10 in which the

understory was modelled as dry litter. In Sim 2.1, it was assumed that EWT dropped in all

canopy layers from 0.013 g cm-2 (0.014 g cm-2 in forest scene 2) to 0.008 g cm-2 (38% and 43%

drop in EWT for forest scenes 1 and 2 respectively), simulating a water stress condition.

In Sim 2.2, canopy layers were dried more, simulating a more severe stress, and lower EWT

value was used (0.004 g cm-2, 69% and 71% drop in EWT from its original value for forest

scenes 1 and 2 respectively). In simulations 2.1 and 2.2, the vertical heterogeneity in EWT was

ignored, and it was assumed that all canopy layers dried simultaneously. Sim 2.3 modelled the

vertical variation in EWT. For forest scene 2, as there were no actual EWT vertical profile

measurements of the trees, Sim 2.3 was not carried out. Simulations 2.4 to 2.9 aimed at

investigating which canopy layers contributed the most to the plot reflectance, by applying

different alterations to the EWT in canopy bottom layers, as shown in Table 7-6, simulating

scenarios of water stress that started in the bottom of the canopy and then spread upwards.

Table 7-6. Group 2 simulations.

Sim Forest scene 1 Forest scene 2

2.1 0.008 g cm-2 in all canopy layers 0.008 g cm-2 in all canopy layers

2.2 0.004 g cm-2 in all canopy layers 0.004 g cm-2 in all canopy layers

2.3 Vertical profiles in Table 7-4 N/A

2.4 Top five layers: EWT vertical profiles

0.008 g cm-2 in remaining layers

Top five layers: 0.014 g cm-2


2.5 Top four layers: EWT vertical profiles


Top four layers: 0.014 g cm-2


2.6 Top three layers: EWT vertical profiles


Top three layers: 0.014 g cm-2


2.7 Top five layers: EWT vertical profiles


Top five layers: 0.014 g cm-2


2.8 Top four layers: EWT vertical profiles




2.9 Top three layers: EWT vertical profiles


Top three layers: 0.014 g cm-2


2.10

Top four layers: EWT vertical profiles


Understory changed to litter



Understory changed to litter

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7.7 Model validation

This research focused on DART simulation of vegetation reflectance, which has been

successfully validated using ground and airborne validation data (Gastellu-Etchegorry et al.,

1999). In addition, de Castro Oliveira et al. (2017) assessed DART simulation accuracy of

Eucalyptus plantations reflectance, by comparing it to reflectance extracted from high

resolution Pleiades satellite imagery, reporting absolute error < 2%. Plus, as part of the

RAdiation transfer Model Intercomparison (RAMI) experiment, DART simulations of

vegetation reflectance were cross-validated against other, independently developed, 3D RTMs,

including FLIGHT, SPRINT, Librat, and Raytran (Pinty et al., 2001; Pinty et al., 2004;

Widlowski et al., 2007; Widlowski et al., 2008; Widlowski et al., 2013; Widlowski et al., 2015).

DART Earth-atmosphere radiative coupling was validated successfully using simulations of the

MODTRAN (MODerate resolution atmospheric TRANsmission) atmosphere RTM (Gascon et

al., 2001; Grau and Gastellu-Etchegorry, 2013).

To evaluate how close the results obtained from the simulations conducted in this study were

to real satellite imagery, and how the assumptions used in parametrizing the model affected the

accuracy of the results, Sentinel-2A imagery that covered Wytham Woods was acquired from

the Copernicus Open Access Hub, processed to BOA reflectance. The imagery was collected

on 22nd of May 2017, which corresponded to the data collection campaign (22nd to 31st of May

2017), in which TLS data collection and leaf EWT measurements were conducted (Chapter 5).

The average zenith angle of the satellite imagery was 2.7º, and the sun zenith angle was 32.4º,

both being close to their assumed values in the simulations, which were 0.0º and 30º,

respectively. Pixels corresponding to the 18 ha Wytham core plot (Figure 5-1) were extracted

from the NIR and SWIR bands (bands 8A and 11, respectively) in the satellite imagery, using

the coordinates specified in Butt et al. (2009). Minimum, maximum, and average reflectance in

the extracted pixels were determined. In addition, NDWI was calculated on a pixel-by-pixel

basis, then the minimum, maximum, and average NDWI values in the plot were determined.

The calculated values were compared to Sim 1.4 results of forest scenes 1 and 2.


7.8.1 Comparing the simulated reflectance to Sentinel-2A data

Sim 1.4 results obtained from forest scenes 1 and 2 were compared to the actual BOA

reflectance and NDWI retrieved from the Sentinel-2A satellite imagery (Figure 7-9). The results

revealed that the simulated NIR reflectance was lower than the average reflectance obtained

138

from the satellite data, referred to as the average actual reflectance hereafter, by 14.8 % and

7.6 % in forest scenes 1 and 2 respectively. As discussed in Section 7.4, forest scene 1 suffered

from occlusion, as 3D tree models used in the scene were reconstructed from leaf-on TLS scans,

making their canopy LAI less than the actual value in the real forest plot. This can explain the

simulated NIR reflectance being less than the actual NIR reflectance obtained from the satellite

data. One approach to solve this issue is by combining TLS data and airborne LiDAR data in

order to achieve a better coverage of canopy top, and thus reduce the occlusion effects.

On the other hand, the simulated SWIR reflectance in both forest scenes was very close to the

average actual reflectance, indicating that the EWT measurements used in the simulations,

retrieved from the TLS data and destructive sampling, were realistic. The simulated SWIR was

6.6 % less than the average actual reflectance in forest scene 1, and 6.4 % higher than the

average actual reflectance in forest scene 2. The simulated NDWI obtained from forest scene 1

was 8 % lower than the average actual NDWI, while forest scene 2 simulated NDWI was 12.5 %

lower than average actual NDWI. Both simulated NDWI values were affected by the simulated

NIR reflectance being lower than the actual NIR reflectance. Furthermore, simulated NDWI of

forest scene 2 could have also been influenced by the simulated SWIR reflectance being higher

than its actual value. No EWT measurements were conducted in field for forest scene 2 and

EWT was assumed to equal 0.014 g cm-2 in all the canopy layers, based on the EWT retrieved

from destructive sampling in forest scene 1, as discussed in Section 7.5.

Overall, the results showed that the simulated reflectance and NDWI in both forest scenes can

be considered realistic, despite the approximations used in parametrizing the forest scenes. It is

worth mentioning that the minimum, maximum, and average satellite reflectance and NDWI

were calculated from all the pixels corresponding to the 18 ha Wytham core plot, and not from

the exact pixels corresponding to the locations of the simulated forest plots, as the accurate

coordinates of the plots were not available.

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Figure 7-9. Comparison between the simulated NIR reflectance, SWIR reflectance, and NDWI,

and the actual mean values retrieved from Sentinel-2A satellite imagery of the plot. Whiskers

represent one standard deviation.

7.8.2 Group 1 simulations

Table 7-7 shows the simulated 864 nm NIR and 1613 nm SWIR reflectance, and the

corresponding NDWI, resulting from both forest scenes.

Table 7-7. Group 1 simulations results. The change in reflectance and NDWI was calculated in

regard to Sim 1.1, except for Sim 1.5.

Forest scene 1

Sim NIR

reflectance

NIR

reflectance

change (%)

SWIR

reflectance

SWIR

reflectance

change (%)

NDWI NDWI

change (%)

1.1 0.3301 --- 0.1489 --- 0.3782 ---

1.2 0.3364 1.9 0.1505 1.1 0.3818 1

1.3 0.4018 21.7 0.1720 15.5 0.4004 5.9

1.4 0.4169 26.3 0.1747 17.3 0.4093 8.2

1.5 0.3948 - 5.3* 0.1965 12.5* 0.3354 - 18.1*

Forest scene 2

1.1 0.3991 --- 0.1863 --- 0.3635 ---

1.2 0.4448 11.5 0.1977 6.1 0.3846 5.8

1.3 0.4042 1.3 0.1876 0.7 0.3660 0.7

1.4 0.4524 13.4 0.1991 6.9 0.3887 6.9

1.5 0.4497 - 0.6* 0.2005 0.7* 0.3834 - 1.4*

* Change in reflectance and NDWI was calculated in regard to the results of Sim 1.4.

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7.8.3 Effects of the woody materials on the plot reflectance

For forest scene 1, the results revealed that the woody materials (Sim 1.2) had made minimal

contribution to the plot reflectance. In comparison to Sim 1.1, the plot reflectance in the NIR

and SWIR bands, and NDWI remained almost unchanged. The negligible effects of the woody

materials on the plot reflectance and NDWI may be a result of the missing branches and small

twigs in the top of the canopy, in the 3D models used to build the forest scene (Figure 7-4), as

a result of occlusion in the TLS data. In a similar study, Malenovský et al. (2008) used DART

simulations, coupled with AISA Eagle imagery, to study the influence of woody materials on

Norway spruce canopy reflectance, reporting that when only trunks and first order branches

were taken into account, a negligible change (< 1 %) was observed in the canopy reflectance.

When small branches and twigs were incorporated into the model, a decrease in the canopy

reflectance was reported, which varied between spectral bands (2% in visible and 4% in NIR).

Another reason may be the woody materials being occluded by the leaves in the canopy top,

thus having a low contribution to top of the canopy reflectance, and to NDWI as a result. A

similar observation was reported by Verrelst et al. (2010), when PROSPECT and FLIGHT

RTM simulations were used to study the influence of woody materials on canopy reflectance

for three coniferous plots, which varied in age and structure. It was reported that the woody

materials’ effects were negligible in the dense, young plot, but were significant in the old-

growth plots, concluding that the woody materials’ influence must be taken into consideration

for mature or partly defoliated forest plots. However, these studies included coniferous forest

plots only, while this research focuses on deciduous broadleaf forest plots, for which there is a

gap in the literature regarding the contribution of the woody materials to the plot reflectance.

By contrast, the results obtained in forest scene 2 showed that the woody materials had some

influence on the plot reflectance and NDWI. NIR reflectance increased by 11.5 %, while SWIR

reflectance increased by 6.1 %, resulting in a 5.8 % increase in NDWI. Unlike forest scene 1,

tree models used in this forest scene did not suffer from occlusion, meaning that higher order

branches and twigs in the canopy, especially in the canopy top, were accounted for and

modelled. This difference in the tree models used in the forest scenes can be the reason for the

different observations obtained in the results.

7.8.4 Effects of the understory on the plot reflectance

The understory had a high contribution to the plot reflectance in forest scene 1 (Sim 1.3). The

NIR and SWIR reflectance increased by 21.7 % and 15.5 % respectively, in comparison to Sim

1.1, causing an increase in NDWI by 5.9 %. On the other hand, the understory had no effect on

141

the plot reflectance and NDWI in forest scene 2. The observed differences between the two

forest scenes was due to the presence of canopy gaps in forest scene 1, while forest scene 2 had

no canopy gaps, and canopy gap distribution is known to affect the contribution of understory

to forest plot reflectance (Guyot et al., 1989; Spanner et al., 1990; Miller et al., 1997; Zarco-

Tejada et al., 2001; Rautiainen and Lukeš, 2015; Landry et al., 2018). Furthermore, forest

scene 1 had less leaves and woody materials in the canopy top than the real forest, because of

occlusion in the TLS data used to reconstruct the plot as previously discussed, which allowed

more irradiance to reach the understory, which in return resulted in the understory having high

contribution to the plot reflectance. In a similar study, Eriksson et al. (2006) used Forest

Reflectance and Transmittance (FRT) RTM to study how understory contributed to top of the

canopy reflectance of 20 forest stands (14 deciduous and six coniferous) in southern Sweden,

30 m × 30 m in size each. It was reported that reflectance values varied by up to ± 18% in the

red region and up to ± 10% in the near infrared region due to the understory. This suggested

that the observed increase in simulated reflectance in forest scene 1 due to the understory was

exaggerated as a result of the missing leaves and branches in the upper canopy layers.

When the effects of woody materials and understory were combined (Sim 1.4), NDWI

increased by 8.2 %, in comparison to Sim 1.1, in forest scene 1, and by 6.9 % in forest scene 2.

When the understory was changed from healthy grass in Sim 1.4 to litter in Sim 1.5, NIR

reflectance dropped by 5.3 % and SWIR reflectance increased by 12.5 % in forest scenes 1,

causing an 18 % drop in NDWI. The NIR, SWIR, and NDWI of forest scene 2 remained almost

unchanged. This showed that NDWI can only detect water stress in the understory if canopy

gaps existed in the forest plot.

More simulations are still needed, with different types of understory (understory being

modelled as turbid cells with different dimensions, density, and optical properties), to further

investigate the contribution of the understory to forest plot reflectance and NDWI. Additionally,

simulations that include bare soil, or add a soil layer beneath the understory, can be used to

study the effects of soil on reflectance.

7.8.5 Group 2 simulations

Table 7-8 shows the simulated SWIR reflectance in both forest scenes, whilst Table 7-9 shows

the simulated NDWI. Sim 1.4 results were used as a reference to calculate the change in SWIR

reflectance and NDWI. NIR reflectance was not included in the results, as it remained

unchanged. The results revealed that the SWIR reflectance and NDWI changed considerably

142

when EWT was decreased in all canopy layers to 0.008 g cm-2 in Sim 2.1, and to 0.004 g cm-2

in Sim 2.2, with the SWIR reflectance increasing and NDWI decreasing. Such change in NDWI

was expected, as NDWI is very sensitive to the change in vegetation moisture content (Serrano

et al., 2000; Jackson, 2004; Dennison et al., 2005; Chakraborty and Sehgal, 2010).

Table 7-8. Group 2 simulated reflectance. The change in reflectance was calculated in regard

to Sim 1.4. Blue corresponds to changing EWT to 0.008 g cm-2, whilst grey corresponds to

changing EWT to 0.004 g cm-2.

Forest scene 1 Forest scene 2

Sim SWIR

reflectance

SWIR reflectance

change (%)

SWIR

reflectance

SWIR reflectance

change (%)

1.4 0.1747 --- 0.1991 ---

2.1 0.2137 22.3 0.2537 27.4

2.2 0.2615 49.7 0.3112 56.3

2.3 0.1786 2.2 --- ---

2.4 0.1809 3.5 0.2087 4.8

2.5 0.1839 5.3 0.2115 6.2

2.6 0.1905 9 0.2194 10.1

2.7 0.1838 5.2 0.2183 9.6

2.8 0.1916 9.7 0.2238 12.4

2.9 0.2072 18.6 0.2402 20.6

2.10 0.2168 24.1 0.2255 13.3

Table 7-9. Group 2 simulated NDWI. The change in NDWI was calculated in regard to Sim 1.4.

Blue corresponds changing EWT to 0.008 g cm-2, whilst grey corresponds to changing EWT to

0.004 g cm-2.

Forest scene 1 Forest scene 2

Sim NDWI NDWI change (%) NDWI NDWI change (%)

1.4 0.4093 --- 0.3887 ---

2.1 0.3237 - 20.9 0.2831 - 27.2

2.2 0.2319 - 43.3 0.1865 - 52

2.3 0.4002 - 2.2 --- ---

2.4 0.3947 - 3.6 0.3689 - 5.1

2.5 0.3879 - 5.2 0.3629 - 6.6

2.6 0.3728 - 8.9 0.3470 - 10.7

2.7 0.3881 - 5.2 0.3503 - 9.9

2.8 0.3701 - 9.6 0.3385 - 12.9

2.9 0.3360 - 17.9 0.3075 - 20.9

2.10 0.2864 - 30 0.3328 -14.4

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Sim 2.3 showed that the EWT vertical heterogeneity almost had no effect on the plot reflectance

and NDWI, both changing by 2.2%. Possible reasons for this were that the vertical

heterogeneity was not significant enough to cause a change in reflectance and NDWI, or that

the upper canopy layers, in which there was minimal vertical heterogeneity, dominated the plot

reflectance. The results of simulations 2.4 to 2.9 confirmed that the top four canopy layers

contributed the most to the plot reflectance and to the change in NDWI. For forest scene 1,

Sim 2.4 showed that reducing EWT to 0.008 g cm-2 in the canopy layers below the top five

layers, while leaving EWT in top five layers unchanged, increased the SWIR reflectance by

only 3.5%, and caused NDWI to drop by 3.6%. This showed that the canopy layers below the

top five layers made a minimal contribution to the plot reflectance and NDWI. When EWT in

canopy layer five was reduced (Sim 2.5), the magnitude of the change in reflectance and NDWI

increased slightly. Reducing EWT in canopy layer four (Sim 2.6) increased the magnitude of

the change in reflectance to 9%, and that of the change in NDWI to 8.9 %. This revealed that

reducing EWT in canopy layer four alone almost had the same effect on reflectance and NDWI

as reducing EWT in all canopy layers below it combined. Furthermore, comparing the change

in NDWI that was observed when all canopy layers were dried down to 0.008 g cm-2 to the

change observed when the top four canopy layers remained healthy (20.9 % drop and 5.2 %

drop, respectively), further showed that NDWI was mainly detecting the change in EWT in

canopy top layers.

Simulations 2.7 to 2.9 further confirmed this, showing that reducing EWT to 0.004 g cm-2 in

canopy layer four alone almost had the same effect on reflectance and NDWI as reducing EWT

in all lower canopy layers combined. The simulations also showed that when EWT in all canopy

layers was reduced to 0.004 g cm-2, NDWI dropped by 43.3 %, while it only dropped by 9.6 %

when the top four canopy layers remained healthy, and all the remaining canopy layers were

dried down. Results obtained in forest scene 2 were consistent with forest scene 1 results, with

some differences in the magnitude of the change in reflectance and NDWI, and thus only forest

scene 1 results were discussed to avoid repetition.

Simulations 2.4 to 2.9 assumed that a water stress started in the canopy bottom then spread

upward, whilst the understory remained healthy, for the sake of studying the effects of reducing

EWT in the lower canopy on the plot reflectance and NDWI. In reality, a water stress caused

by drought conditions will also affect the understory. Sim 2.10 accounted for this, by drying all

canopy layers below the top four canopy layers to 0.004 g cm-2, and modelling the understory

as dry litter. The simulation was a repeat of Sim 2.8, with the only change being modelling the

144

understory as litter instead of healthy grass. Comparing the results of the two simulations in

forest scene 1 showed that drying the understory had a significant effect on the NDWI, while it

had a minimal effect on the NDWI in forest scene 2. As discussed in Section 7.8.4, forest

scene 1 had less leaves and branches in the canopy top than in reality, which increased the

contribution of the understory to the plot reflectance and NDWI, whilst Forest scene 2 was a

more realistic representation of the forest.

Overall, results of group 2 simulations showed that care must be taken while using spaceborne

and airborne optical RS data to monitor forest health and early-detect the risk of wildfires,

especially in dense forests, as the upper canopy layers, which have more moisture, were found

to dominate the forest plot reflectance. In the case of moderately dense and open forests, NDWI

may reflect the severity of the water stress, even if the upper canopy layers remained healthy,

as a result of the understory contribution to the plot reflectance and NDWI. On the other hand,

in dense forests, a spaceborne sensor will not be able to detect a severe water stress in the

understory and lower canopy until the upper canopy layers begin to get stressed.

The results presented in this chapter cannot be generalized until more forest plots, sensors, and

case studies are simulated. Also, similar case studies need to be simulated using other 3D RTMs

for cross-comparison with the results obtained from DART. The results of RTM simulations

are very sensitive to the phase function used in single and multiple scattering of radiation in the

canopy, and also to the number of successive orders of scattering (Widlowski et al., 2015). The

results of the RAMI-IV experiment, in which highly realistic forest scenes were simulated,

showed variation in the simulated spectra between the different 3D RTM models tested

(Widlowski et al., 2013).

7.9 Summary

This chapter integrated the vertical heterogeneity of forest canopy EWT in the RTM DART,

aiming at investigating how such heterogeneity would affect the forest plot reflectance and the

NDWI. In addition, various case scenarios were simulated to study the effects of woody

materials and understory on the plot reflectance, and also to examine how sensitive NDWI was

to the change in the plot EWT. Simulations that included decreasing EWT in all canopy layers

simultaneously, and others that involved drying the canopy bottom layers only, were conducted

and compared, in an attempt to determine which canopy layers dominated the plot reflectance

and contributed the most to the change in NDWI. Two forest scenes were used in the

simulations, each of them representing a different forest plot in the same forest. The forest

145

scenes had different tree structure and species, and their tree 3D models were reconstructed

from TLS data using two different approaches. The 3D leaf models of the trees were divided

into a number of layers, with a thickness of 1 m each, in order to assign different optical

properties to each layer, based on the EWT of it, and thus modelling the vertical variation of

EWT in the forest plot.

The NIR and NDWI calculated from Sentinel-2A satellite imagery showed that the simulated

reflectance and NDWI of the two forest scenes were realistic, lying within the minimum and

maximum values observed in the satellite imagery, and not far from the mean. Forest scene 2

was more efficient in terms of the required processing time and computational resources. Each

simulation required approximately six hours of processing time using forest scene 1, while the

same simulation required approximately one hour and 15 minutes to complete using forest

scene 2, using the same PC.

The results of the simulations revealed that the understory had a high contribution to the forest

plot reflectance and affected the NDWI in forest scene 1, which suffered from occlusion and

contained canopy gaps, while it had a minimal effect on the reflectance and the NDWI in forest

scene 2, which was a more realistic representation of the forest. These findings agreed to what

was previously reported in the literature, and further showed that the effects of the understory

on the forest plot reflectance must be accounted for in moderately dense and open forests. The

woody materials had a minimal effect on the plot reflectance and NDWI in forest scene 1,

mainly because the woody materials in upper canopy were missing from the tree 3D models

used in reconstructing the plot. On the other hand, the woody materials had some influence on

forest scene 2 reflectance and NDWI, but the effects were minimal in comparison to the changes

in NDWI caused by reducing EWT.

Modelling the vertical heterogeneity of EWT had a negligible effect on the plot reflectance, in

comparison to assuming a uniform EWT in all canopy layers. However, it was found that this

was caused by the top four canopy layers dominating the plot reflectance. Drying the canopy

layers below the top four layers, while maintaining upper canopy layers healthy, changed

NDWI in a way that did not reflect the severity of the water stress and that the lower canopy

layers lost approximately 70 % of their EWT. This may cause misjudgements while monitoring

the forest health, during drought conditions or in case of water stress that started in the canopy

bottom then spread upward, caused by disease or pest invasion. The drying patterns of forest

canopies still need to be studied using TLS to examine how the EWT vertical profiles would

146

change during drought conditions, and to investigate whether the trees would lose moisture

equally from all canopy layers, or would attempt to maintain the upper canopy layers healthy

to optimize photosynthesis.

The results obtained in this chapter may be specific to the forest under study, or to the species

in the forest, or specifically to broadleaf deciduous forests, and thus cannot be generalized,

unless more forest types and species are studied. Also, similar case studies need to be simulated

using other RTMs for cross-comparison with the results obtained from DART.

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Chapter 8. Discussion and conclusions

8.1 Research motivation

Determining the vegetation water status can reflect the physiological status of vegetation, thus,

it has significant importance for various agricultural and forestry applications (Peñuelas et al.,

1994; Chaves et al., 2003). It can help in early detection of vegetation stress, symptoms of

disease, and signs of pest infestations in forests and agricultural crops (Carter, 1993; Datt, 1999;

Trumbore et al., 2015). Additional applications in forestry include early detection of wildfire

risk, forest fire modelling, and studying wildfire regimes (Ustin et al., 1998; Chuvieco et al.,

2004a; Danson and Bowyer, 2004; Yebra et al., 2008). Furthermore, in agricultural crops,

monitoring vegetation water status is important for drought assessment, irrigation scheduling,

and crop yield estimation (Peñuelas et al., 1992; Sepulcre-Cantó et al., 2006).

Optical RS data, both spaceborne and airborne, have been widely utilized in monitoring the

vegetation water status at a landscape level (Dash et al., 2017). Typically, canopy EWT is

estimated directly from optical RS data and used as an early indicator of vegetation stress.

However, the accuracy of estimating EWT from optical RS data is influenced by canopy

structure, background soil and understory reflectance, atmosphere, and shadows (Ali et al.,

2016). Although using RTMs to estimate EWT instead of using simple vegetation indices can

overcome these limitations, the models do not account for the vertical heterogeneity in canopy

biophysical and biochemical traits. Such heterogeneity plays a role in the canopy reflectance,

and ignoring it can reduce the accuracy of the estimation of vegetation biochemical traits

(Kuusk, 2001; Wang and Li, 2013). Methods to quantify such heterogeneity are still needed.

Also, there is a need for a fast and reliable method to measure canopy EWT in sampling plots

that match the pixel size of satellite sensors to be used in the calibration and validation of the

EWT estimation models. The in-situ destructive sampling approaches currently being used are

insufficient, especially for satellites with large pixel size such as MODIS. Another limitation

of using optical RS data to estimate EWT is that EWT cannot be measured predawn, which is

a more reliable indicator of vegetation water status (Améglio et al., 1999). Dual-wavelength

TLS was identified as a tool that can overcome the limitations associated with optical RS

estimation of EWT, by providing 3D EWT estimates that can be used to study the EWT vertical

heterogeneity. TLS can also serve as a calibration and validation tool for satellite estimation of

EWT. In addition, EWT can be estimated both predawn and at midday, as TLS instruments are

active sensors.

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8.2 Revisiting research aim and objectives

The main aim of this research was to estimate canopy EWT in 3D using commercial dual-

wavelength TLS, both in a laboratory experiment and multiple field campaigns, to quantify and

better understand the canopy EWT vertical heterogeneity. The intensity data from

commercially available TLS instruments, operating at NIR wavelength (the Leica P20) and

SWIR wavelength (the Leica P40 and P50) was combined into a vegetation index (NDI), after

developing robust methods to calibrate the intensity to apparent reflectance (Chapter 3). NDI

was used to generate 3D canopy EWT estimates in an indoors dry-down experiment (Chapter 4)

and four data collection campaigns. The main data collection campaign was in a mixed-species

forest plot in Wytham Woods, Oxford, and is named hereafter as the Wytham dataset (Chapter

5). The second data collection campaign was in a willow crop site in Newcastle University

Cockle Park Farm in Ulgham, Northumberland, and is named hereafter as the willow dataset

(Chapter 6). The third and fourth campaigns were carried out in a mixed-species tree plot in

Exhibition Park, Newcastle. The plot was scanned at the end of a heatwave (August 2018) and

two months later (October 2018), resulting in two datasets, named hereafter as Exhibition Park

August dataset and Exhibition Park October dataset (Chapter 6). Vertical profiles of EWT were

generated and compared within and across species.

Additional aims included: (1) detecting EWT temporal changes in 3D, which was addressed

using the indoors dry-down experiment (Chapter 4) and Exhibition Park datasets (Chapter 6),

and (2) investigating how EWT vertical heterogeneity affected forest plot reflectance and

received satellite signal, which was addressed by modelling the 3D EWT estimates of the

Wytham dataset in the DART model (Chapter 7). Four TLS instruments were utilized in this

study: a Leica P20 instrument, two Leica P40 instruments, and a Leica P50 instrument. This

research had seven main objectives, which were addressed as follows:

Objective one: to develop robust methods to calibrate the intensity data from commercially-

available TLS instruments to apparent reflectance.

This objective was addressed in Chapter 3. The intensity – range relationship was investigated

separately for each instrument using external reference targets with known reflectance,

revealing that the four instruments deviated from the 1/R2 component of the laser equation. This

was caused by the near-distance intensity reducer that aimed at protecting the optics and the on-

board range calibration adjustments that attempted to correct the intensity for the range effect

after a specific range, which varied between the instruments. This deviation from the laser

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equation was expected, as similar observations were previously reported for numerous

commercial TLS instruments, e.g., the Faro LS880 and Leica HDS 6100 (Kaasalainen et al.,

2011), the Riegl LMS Z420i (Blaskow and Schneider, 2014), the Z+F Imager 5006i (Blaskow

and Schneider, 2014; Fang et al., 2015), the Faro Focus3D 120 (Tan et al., 2016), the Riegl

VZ400i (Xu et al., 2018), and the Riegl VZ4000 (Tan et al., 2019). As each of the four

instruments used in this study was found to have its own unique intensity – range relationship,

an intensity correction model was developed for each instrument separately. The intensity –

reflectance relationship was then investigated for each instrument. This revealed non-linear

relationships as a result of the internal adjustments performed by the instruments to alter the

intensity values and enhance the visual appearance of the point clouds. When the raw intensity

data were extracted from the instruments using an intensity map editor provided by the

manufacturer, the relationship was found to be almost linear, with some slight non-linearity.

The intensity correction models were validated using independent reference targets. The

average error observed in all validation experiments at all ranges tested was 2.6 %, while the

minimum and maximum average errors were 1.1 % (RMSE = 0.011) and 5.1 %

(RMSE = 0.043) respectively. The errors observed at ranges less than 4 m, especially at 2 m

range, were higher than all remaining ranges, with errors > 10 %. This was a result of the

intensity correction models not being able to fully correct the intensity for the near distance

intensity reducer effects. This must be considered if scans were planned at near ranges, or if

objects of interest were less than 4 m away from the instrument, such as the understory

vegetation surrounding the scanning positions in a forest plot. The accuracy of the intensity

correction models was considered sufficient for the application, which was further confirmed

with the high correlation obtained between NDI and EWT at leaf and canopy levels, as

described in the upcoming sections.

Objective two: to investigate the ability of NDI to minimize the effects of incidence angle and

leaf internal structure without the need for further radiometric corrections.

This objective was addressed in Chapter 3. For the incidence angle effects, eighteen leaf

samples from six different tree species, including grey alder, common lime, common alder,

hornbeam, poplar, and cherry, were scanned. The incidence angle was changed between 0 and

60 degrees, with scans conducted at 20 degrees intervals, and NDI was calculated for each

incidence angle. The incidence angle effect on each wavelength separately was severe, as the

change in incidence angle between zero and 60 degrees reduced the SWIR reflectance by an

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average of 47 % and the NIR reflectance by an average of 52 %. Using NDI largely minimized

the incidence angle effects for all six species. Changing the incidence angle between zero and

40 degrees caused an average change in NDI of 6.7 % across all leaf samples, whilst changing

it between zero and 60 degrees caused an average change of 13.7% in the NDI. One reason for

the more significant change in NDI at 60 degrees incidence angle was that leaves were not

perfectly Lambertian surfaces, and the reflectance – incidence angle relationships deviated

more from the cosine law as the incidence angle increased towards 60 degrees.

For the same leaf samples, the change in NDI between minimum and maximum EWT values

was 53 %. In the Wytham dataset, the change in EWT caused 79 % change in NDI, and overall

in all leaf samples measured in this study, excluding holly leaf samples, the change in EWT

caused a 127 % change in NDI. This showed that EWT had more significant impact on NDI

than the remaining effects of the incidence angle. Hancock et al. (2017) reported similar

observations for NDI of 1064 nm and 1545 nm, also observing deviations at some incidence

angles, for instance, at 10, 40, and 60 degrees incidence angles, which was higher than the

deviation observed at 60 degrees in this study. It was concluded that the change in NDI caused

by the incidence angle effects was negligible in comparison to the change in NDI caused by

EWT. However, at the canopy level in complex vegetation environments such as forests, the

remaining effects of the incidence angle on NDI may contribute to the errors in estimating

EWT, as the laser beams will hit the leaves at all possible incidence angles (Kaasalainen et al.,

2018).

The ability of NDI to minimize the leaf internal structure effects was investigated by conducting

2886 PROSPECT simulations to examine how the leaf structure coefficient (N), representing

leaf thickness and leaf internal cellular arrangements, and LMA affected the NDI – EWT

relationship. LMA was found to have a minimal effect on NDI, as the maximum change in

LMA across all species examined caused only a 7 % increase in NDI. N at the canopy level was

found to range between 1.5 and 2 for green canopies, and was ≥ 2.5 for senescent canopies, as

shown in Figure 8-1. Increasing N between 1.5 and 2 would result in a 10 % decrease in NDI

according to the PROSPECT simulations. As N and LMA are correlated variables, with an

increase in N typically corresponding to an increase in LMA, their combined effects on NDI

would be further reduced as the effects have opposite directions. However, the effects would

not entirely cancel each other out, and N would have some remaining effects on NDI. This can

be a factor contributing to the variation of EWT estimation errors observed at the canopy level

in Wytham and Exhibition Park datasets. However, this did not prevent the use of a pooled

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EWT estimation model to successfully estimate EWT at the canopy level in mixed-species sites.

Increasing N to 2.5 would cause a 17 % decrease in NDI, while increasing it to 3 would cause

a 22 % decrease in NDI. Thus, species that had significantly thicker leaves than the other species

in the plot, for instance, holly leaves in Exhibition Park dataset, needed their own NDI – EWT

estimation model. Furthermore, an NDI – EWT relationship developed using green leaves could

not be applied to senescent leaves.

Figure 8-1. The NDI – EWT relationship for N = 1.5 (black), N = 2 (green), and N = 2.5 (blue),

resulted from PROSPECT simulations, in addition to the NDI – EWT relationship at canopy

level for all scanned trees in this study, excluding holly trees.

Overall, the experiments conducted in this study showed that EWT was the main factor deriving

the change in NDI, whilst the incidence angle and leaf internal structure remaining effects on

NDI may affect the accuracy of EWT retrieval at the canopy level.

Objective three: to examine the relationship between NDI and EWT at the leaf level across a

range of species.

This objective was addressed in Chapter 4, Chapter 5, and Chapter 6. A total of 192 leaf samples

were collected, scanned, and their NDI and EWT measured. The samples represented thirteen

different species as follows: snake-bark maple, grey alder, common lime, common alder,

poplar, cherry, sycamore, oak, beech, ash, Swedish whitebeam, holly, and six varieties of

willows. Among the 192 leaf samples, 137 leaves were green, 19 leaves were senescing,

meaning they had started to change colour but were still relatively green (Exhibition Park

October dataset), and 36 leaves were senescent, meaning they had already changed colour and

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lost nutrients and chlorophyll content (willow dataset). For each individual species, moderate

to high correlation was observed between NDI and EWT, with R2 ranging between 0.55 and

0.94.

For green leaves, site-specific species-independent pooled NDI – EWT models were fitted with

high accuracy (R2 ranging between 0.88 and 0.94), in addition to a site- and species-independent

pooled NDI – EWT model that combined all leaf samples (R2 = 0.91, Figure 6.19). This

concurred with the results obtained from the PROSPECT simulations and showed that the

remaining effects of leaf internal structure on the NDI – EWT relationship did not prevent

fitting pooled NDI – EWT models with high accuracy. This suggested that the NDI – EWT

relationship was species-independent, agreeing with the results obtained in Gaulton et al. (2013)

for NDI of 1064 nm NIR and 1545 nm SWIR laser wavelengths. On the other hand, NDII and

NDWI, the vegetation indices equivalent to NDI in optical RS data, have also been previously

linked to EWT in mixed-species sites (Cheng et al., 2008a; Yilmaz et al., 2008; De Jong et al.,

2014). However, other studies highlighted that in the case of a large variation in leaf thickness

and LMA between species, the accuracy of NDII and NDWI estimation of EWT can be

significantly reduced (Zarco-Tejada et al., 2003; Zhang and Zhou, 2015). For NDI, this was

observed in holly leaf samples, which was the only species that did not fit in the pooled NDI –

EWT models. Holly leaves were clearly thicker than the leaves of the other species, and had

145 % higher LMA than the average LMA measured for the other species in this study. In

addition, the shiny surface of holly leaves also contributed to the deviation of the NDI – EWT

relationship of this species from that of the other matt leaves, as Zhu et al. (2017) showed that

at 1550 nm wavelength, shiny leaves had stronger specular reflection. The higher reflectance at

the 1550 nm wavelength reduced the NDI and thus contributed to the deviation in the NDI –

EWT relationship.

The sycamore leaves diseased with powdery mildew also did not follow the NDI – EWT

relationship of healthy leaves, and similar to holly leaves, did not fit in the pooled NDI – EWT

models. The powdery mildew that covered sycamore leaf samples decreased the NIR

reflectance but had a minimal effect on the SWIR reflectance, resulting in very low NDI values

in comparison to NDI of healthy leaves. This concurred with the findings of Yuan et al. (2014)

for winter wheat powdery mildew. In general, diseases are known to reduce leaf reflectance in

NIR (Nilsson, 1991), and NIR wavelengths have been adopted in detecting powdery mildew

infections in winter wheat (Zhang et al., 2012; Yuan et al., 2014) and grape (Beghi et al., 2017).

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However, further investigation is still needed as fungus that causes powdery mildew differs

between plant species.

For the senescent leaves, they did not follow the NDI – EWT relationship of green leaves,

mainly because senescence is known to significantly change the leaf internal cell structure

(Jacquemoud and Baret, 1990; Buchanan-Wollaston, 1997), and such changes were found to

influence the NDI – EWT relationship, as shown in Figure 8-1. It was possible to fit site-specific

pooled NDI – EWT models for the Exhibition Park October dataset and willow dataset

separately, but not a site-independent pooled model that combined all senescent leaves, most

likely because they were at different levels of senescence.

The leaf level results obtained in this research showed that the NDI – EWT relationship was

site- and species-independent to an extent, but highlighted that leaf surface characteristics, leaf

senescence, and leaf thickness must be taken in consideration while deriving pooled, multi-

species NDI – EWT models.

Objective four: to use NDI to generate 3D EWT estimates at the canopy level in a controlled

laboratory experiment, as well as in field campaigns.

This objective was addressed in Chapter 4, Chapter 5, and Chapter 6. Canopy EWT was

successfully estimated in an indoors dry-down experiment and four field campaigns, with errors

in EWT estimates for individual trees ranging between 1 % and 13.5 %. The average errors

observed in the dry-down experiment were 2.9 % and 2.6 % for the deciduous and coniferous

canopies respectively. The average errors obtained in the Wytham dataset, willow dataset,

Exhibition Park August dataset, and Exhibition Park October dataset were 7.7 %, 8.9 %, 7.8 %,

and 4.2 % respectively, using the species-specific NDI – EWT models. When the site-specific

pooled NDI – EWT models were used, the errors were 6.3 %, 6.6 %, 4.8 %, and 5.4 % for the

Wytham dataset, willow dataset, Exhibition Park August dataset, and Exhibition Park October

dataset respectively. It is worth mentioning that for the willow dataset, it was not possible to

estimate EWT on a point-by-point basis as a result of the low registration accuracy resulting

from the strong wind on the day of the scan, and it was only possible to estimate the average

EWT in each plot.

The site- and species-independent pooled NDI – EWT model that combined all green leaves

(Figure 6.19, Equation 6.22) was also successfully used to retrieve canopy EWT estimates in

the Wytham dataset and Exhibition Park August dataset, with average errors of 6.6 % and 10 %

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respectively. The advantage of using pooled EWT estimation models over using species-

specific models is that it does not require prior tree species classification, thus reducing the

processing time, as a single model will be applied to the whole NDI point cloud at once.

However, the results obtained using the site- and species-independent pooled NDI – EWT

model showed that the NDI – EWT relationship was site-specific to an extent, as severe error

in EWT estimation was observed in one beech tree (25 %) when the model was used, in

comparison to a 12 % error in EWT estimation when the site-specific pooled model was used.

As shown in Figure 8-1, this specific beech tree had the lowest EWT and the thinnest leaves

(N < 1.5). Further experiments that include accurate measurements of leaf thickness are still

needed to determine the boundaries outside which pooled NDI – EWT models should not be

used. The powdery mildew that covered the leaves of sycamore trees in the Exhibition Park

August dataset rendered this EWT estimation approach inapplicable at the canopy level as high

error was obtained (47 %). This showed that this EWT estimation approach cannot be applied

if leaves were covered with a material that did not affect the two wavelengths included in the

NDI in a similar manner. However, as the diseased trees would appear as outliers in comparison

to the healthy trees in the canopy level NDI – EWT relationship, this approach can serve as a

fast, non-destructive method to detect diseased trees, which would be identified by their very

low NDI values (Figure 8-2).

Figure 8-2. Using NDI to detect diseased trees. The Sycamore tree, diseased with powdery

mildew (red), had significantly lower NDI than the healthy trees.

The errors obtained at the canopy level in the field campaigns were higher than the errors

reported in Zhu et al. (2017) (mean error of 4.46 %), which, to our knowledge, was the only

previous study in which 3D EWT estimates were generated at the canopy level using TLS.

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However, the aforementioned study was conducted in controlled laboratory settings, utilized a

single-wavelength TLS (1550 nm), required radiometric corrections for the incidence angle

effects, and involved small, individual canopies. Junttila et al. (2019) successfully used dual-

wavelength TLS to detect European spruce bark beetle infestation symptoms in Norway spruce

trees in field campaigns, but 3D EWT estimates at the canopy level were not generated. Overall,

very high correlation was observed between TLS estimated canopy EWT and actual canopy

EWT measured from destructive sampling (Figure 8-3, R2 = 0.95, RMSE = 0.0008 g cm-2). The

results of the conifer tree in the dry-down experiment were excluded, as the tree had very high

EWT, and including the results would have biased the regression and increased R2 to 0.99.

Similarly, holly trees also had relatively higher EWT than all the remaining species. When holly

trees were excluded, the correlation remained high (R2 = 0.82, RMSE = 0.0008 g cm-2). The

accuracy of the EWT estimation at the canopy level was considered high and was comparable

to the accuracy reported in the literature using optical RS data (R2 ranging between 0.7 and

0.92) (Champagne et al., 2003; Zarco-Tejada et al., 2003; Cheng et al., 2008a; Yilmaz et al.,

2008). However, the accuracy of this EWT estimation approach still needs to be investigated at

the plot level for a more accurate comparison to the accuracy of spaceborne and airborne optical

RS estimation of canopy EWT, which will require coupling the EWT estimates with canopy

LAI measurements.

Figure 8-3. The relationship between TLS estimated canopy EWT and actual canopy EWT

measured from destructive sampling for all trees involved in this study.

Overall, the results obtained in this study showed that dual-wavelength TLS can successfully

retrieve canopy EWT in field campaigns conducted in sites that were heterogeneous in terms

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of species and canopy structure. Such heterogeneous sites are more challenging when it comes

to estimating canopy EWT because of the variation in canopy LAI and leaf internal structure

between species (Zarco-Tejada et al., 2003).

Objective five: to study EWT vertical heterogeneity within canopy and determine how it varies

across different species and also between individual trees within each species.

This objective was addressed in Chapter 4, Chapter 5, and Chapter 6. Vertical profiles of EWT

were generated for deciduous snake-bark maple and coniferous Corsican pine canopies in the

indoors dry-down experiment, for twelve forest canopies from three different species in the

Wytham dataset (sycamore, oak, and beech), and for six trees from three different species in

the Exhibition park datasets, with vertical profiles being generated twice, once in August and

once in October (ash, Swedish whitebeam, and holly). Vertical heterogeneity in canopy EWT

was observed in all twenty trees. This concurred with the findings reported in the few studies

found in the literature that investigated the vertical distribution of EWT at the canopy level (Liu

et al., 2015; Arellano et al., 2017; Zhu et al., 2017; Gara et al., 2018), all reporting vertical

heterogeneity.

One reason for the observed EWT heterogeneity was that leaves at different heights within the

canopy contribute differently to the total canopy photosynthesis (Aber, 1979; Ellsworth and

Reich, 1993), and trees tend to dedicate more nutrients and water to sun leaves in the canopy

top than to the shaded leaves in the canopy bottom, to optimize photosynthesis (Hirose and

Werger, 1987; Hikosaka, 2004). This can explain the behaviour observed in the snake-bark

maple and Corsican pine canopies in the indoors dry-down experiment, in the twelve forest

canopies in the Wytham dataset, and in the two ash trees in the Exhibition Park datasets, in

which EWT was higher in the canopy top, gradually getting lower towards the canopy bottom.

Previous studies in the literature also reported that EWT was always higher in the canopy top

than in the canopy bottom (Liu et al., 2015; Arellano et al., 2017; Zhu et al., 2017; Gara et al.,

2018). Mooney et al. (1977) and Chavana‐Bryant et al. (2016) showed that leaf age also played

a role in EWT distribution within a canopy, reporting that younger leaves in the canopy top had

higher EWT than older leaves in the canopy bottom. However, the two Swedish whitebeam and

the two holly trees in the Exhibition Park datasets showed different behaviour, as EWT was not

always the highest in the canopy top and the lowest in the canopy bottom as observed in all

other trees. Typically, sun leaves grow in the canopy top because the top layers of the canopy

receive the majority of irradiance (Chazdon and Fetcher, 1984). However, in the Exhibition

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Park tree plot, there was a wide gap in the canopy in the middle of the plot, thus sun leaves were

not necessarily in the canopy top only, depending on how the trees were illuminated.

The EWT vertical profiles observed could also be a result of the LMA distribution within the

canopy, as EWT and LMA were found to be highly correlated in the destructive sampling

conducted in Chapter 5, suggesting that a leaf with higher LMA, typically thicker, would be

able to hold more moisture than a thinner leaf of the same species. This agreed with the findings

of Junttila et al. (2019), also reporting high correlation between EWT and LMA at the leaf

level. Arellano et al. (2017) and Gara et al. (2018) showed that EWT and LMA vertical profiles

showed similarities, with canopy layers with higher LMA having higher EWT. This can again

be related to the illumination conditions and the distribution of sun/shade leaves within the

canopy, as sun leaves are typically thicker and have higher LMA than shade leaves

(Lichtenthaler et al., 1981).

Objective six: to investigate the potential of using TLS to detect temporal changes in EWT due

to drought conditions.

This objective was addressed in Chapter 4 with an indoors dry-down experiment and in

Chapter 6 with the Exhibition Park August and October datasets. In the dry-down experiment,

TLS was able to detect the change in EWT over a period of eight days for the deciduous snake-

bark maple tree and over a period of nine days for the coniferous Corsican pine tree, while the

trees were drying naturally. TLS estimated a 15 % drop in EWT between the first and last days

of the experiment for the deciduous tree, and a 3.4 % decrease in EWT for the conifer tree.

Furthermore, TLS could detect the daily change in EWT for the deciduous tree, showing that

the tree lost approximately 3.7 % EWT per day in the first two days of the experiment, and

1.6 % EWT per day in the remaining five days. The ability of TLS to detect such fine changes

in EWT suggested that there could be a potential in using TLS to detect canopy EWT changes

throughout the day, and compare predawn EWT to midday EWT to quantify how much

moisture plants lose during photosynthesis. Spaceborne and airborne optical RS sensors can

neither measure such fine changes in EWT because of their temporal resolution limitations, nor

can they provide predawn EWT estimates. Also, determining the predawn vegetation water

status by measuring predawn leaf water potential using a pressure chamber is a very challenging

process if the aim was to determine the water status of a large number of plants (Vila et al.,

2011), and TLS can be a more efficient alternative approach.

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Although the measured daily change in EWT in the dry-down experiment was less than the

errors observed in canopy EWT estimates in the field campaigns, this did not necessarily

indicate that TLS cannot detect the fine changes in EWT in outdoors conditions. For instance,

scanning a forest plot predawn and rescanning it at midday, from the exact same scanning

positions and in non-windy conditions, can theoretically lead to similar errors in canopy EWT

estimates predawn and midday, which can lead to accurate detection of the EWT fine changes.

This has not been tested in this study, and although the ability of TLS to detect EWT temporal

changes in outdoors conditions was examined in the Exhibition Park datasets, the experiment

involved green and senescent leaves, which required the use of two different EWT estimation

models to account for the significant changes in leaf internal structure. This reduced the

accuracy of detecting the change in EWT and thus the results obtained could not be considered

a reference to determine the ability of TLS to measure EWT fine changes.

In the Exhibition Park datasets, the errors in EWT estimates in the August dataset (average error

of 4.8 %) were less than the errors in the October dataset (average error of 5.4 %), which could

have been a result of leaf senescence. The effect of senescence on leaf internal structure is

known to vary between species, and can also vary between leaves from the same species if they

were at different levels of senescence (Buchanan-Wollaston, 1997). Thus, it can be more

challenging to build a NDI – EWT model that can accurately represent all levels of senescence

in the plot. Another source of errors was the wind effect, as there was a gentle breeze in October

during the scan.

The change in EWT between August and October was detected for seven trees from four

different species: ash, Swedish whitebeam, beech and holly. The results showed that the

direction and magnitude of the change in EWT was successfully characterized using TLS. This

showed the potential of this method to be used in detecting the impact of drought on vegetation.

With the very high temporal resolution of TLS, being independent of solar illumination or

limited by cloud coverage, it can be used to fill the gaps in time series produced from optical

RS data. The accuracy of detecting the change in EWT appeared to be mainly a function of the

EWT estimation errors on both dates. An overestimation or underestimation of EWT in both

dates, with similar magnitude of errors, produced the most accurate estimation of the change in

EWT. A higher magnitude of error in one dataset than in the other, or overestimating EWT in

one dataset and underestimating it in the other, resulted in less accurate estimates of the change

in EWT.

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Objective seven: to develop methods to utilise the 3D EWT estimates in the DART model and

simulate the effects of EWT vertical heterogeneity on satellite signal.

This objective was addressed in Chapter 7. Two forest plots were reconstructed in the DART

model, based on TLS data collected in Wytham Woods: a mixed-species oak and sycamore

plot, named hereafter as forest plot 1, and a mixed-species sycamore, ash, and hazel plot, named

hereafter as forest plot 2. Forest plot 1 was reconstructed using the leaf-on TLS point clouds

collected in the Wytham dataset, thus there were missing leaves and wood components in the

canopy top as a result of occlusion. Forest plot 2 was reconstructed from leaf-off scans

conducted with a Riegl VZ-400 TLS instrument, which was used to build the wood component

3D models, then a leaf insertion algorithm was used to add leaves to the 3D models, based on

leaf-on scans conduced with the same instruments (full details in Calders et al. (2018)).

Simulations were carried out to investigate which canopy layers dominated the forest plot

reflectance by assuming equal EWT in all canopy layers, then reducing EWT in canopy bottom

layers only while maintaining EWT constant in canopy top layers. This was achieved by

splitting each tree model as a number of horizontal layers, and assigning different optical

properties to each layer separately. Sentinel-2A NIR and SWIR bands were simulated, and

NDWI was calculated for each case study. The results obtained in the two forest plots revealed

that the plot reflectance, NDWI, and the change in NDWI, were dominated by the reflectance

from the top four meters of canopy. Reducing EWT in all canopy layers by 70 %, simulating a

severe drought condition, caused a 52 % drop in NDWI, which reflected the severe vegetation

water stress. However, reducing EWT by 70 % in all layers below the top four metres of canopy,

whilst the top four metres maintained the high EWT values, resulted in only a 13 % drop in

NDWI, which may not indicate that the forest plot was at risk of wildfire.

Furthermore, comparing the simulated reflectance and NDWI when EWT was equal in all

canopy layers to the simulated reflectance and NDWI when the actual EWT vertical profile was

used showed that the EWT vertical heterogeneity had no effect on the satellite-measured

reflectance or NDWI. This was a result of the majority of the plot reflectance resulting from the

top four metres of the canopy, in which there was minimal vertical heterogeneity. It is worth

mentioning that drying the canopy bottom layers only while maintaining the canopy top layers

healthy did not necessarily represent the real drying patterns of trees during drought conditions,

which still needs to be examined in forest canopies. Overall, the simulations showed that care

must be taken while using spaceborne optical RS data in monitoring forest health and early-

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detection of wildfire risk, especially in dense forests, as the upper canopy layers, which have

more moisture, were found to dominate the forest plot reflectance. In dense forests, a

spaceborne sensor will not be able to detect severe water stress in the understory and lower

canopy until the upper canopy layers begin to get stressed. However, more 3D RTMs still need

to be tested, as the results of RTM simulations are very sensitive to the phase function used in

single and multiple scattering of radiation in the canopy, and also to the number of successive

orders of scattering (Widlowski et al., 2015). The results of the RAMI-IV experiment showed

variation in the simulated spectra between the different 3D RTM models tested (Widlowski et

al., 2013).

Additional aims of the simulations were to study the influence of woody materials and

understory vegetation on the forest plots reflectance. It was found that the woody materials had

no contribution to the plots’ reflectance and NDWI in forest scene 1, most likely because the

woody materials in the upper canopy were missing as a result of occlusion, while the woody

materials had some influence on the reflectance and NDWI in forest scene 2. This agreed with

the results of DART simulations conducted in Malenovský et al. (2008), and FLIGHT RT

simulations conducted in Verrelst et al. (2010), with both reporting that the woody materials

had a low effect on the forest plot reflectance, and the latter showing that the effects increased

in old, open forest canopies. The understory had some effects on forest plot 1 reflectance, as

the plot had some canopy gaps, in addition to the missing leaves in the canopy top layers that

allowed more irradiance to reach the understory. On the other hand, the understory had minimal

effects on forest plot 2 reflectance, as the plot had no gaps and also did not suffer from

occlusion. This agreed to with what has been previously reported in the literature that tree

density and canopy gap distribution determined the contribution of understory to forest plot

reflectance (Guyot et al., 1989; Spanner et al., 1990; Miller et al., 1997; Zarco-Tejada et al.,

2001; Rautiainen and Lukeš, 2015; Landry et al., 2018).

8.3 Suggestions for future research directions

Towards using TLS to quantify other vegetation water status metrics:

This study showed that canopy EWT can be retrieved successfully in 3D using dual-wavelength

TLS data. The 3D EWT estimates can then be used to retrieve other key vegetation water status

metrics. As shown in Chapter 7, tree 3D models can be generated from TLS point clouds and

used to estimate canopy LAI. Previous studies in the literature also showed that canopy LAI

can be successfully estimated from TLS data (Moorthy et al., 2008; Antonarakis et al., 2010;

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Zheng et al., 2013; Vincent et al., 2015; Meng et al., 2019). Combining the 3D EWT estimates

with LAI measurements can lead to estimating canopy water content (kg m-2), as CWC is the

product of EWT and LAI. The CWC estimated from TLS is expected to better-represent the

whole canopy than that retrieved from optical RS data, as it is based on 3D EWT estimates.

Tree 3D models generated from TLS point clouds can also be used to estimate the total leaf

surface area in each canopy layer. Coupling the EWT vertical profiles with the leaf surface area

estimates can lead to quantifying the water content in each canopy layer (kg), which allows the

characterization of whole-tree leaf water status and total water content (kg).

Furthermore, this research focused only on studying the relationship between NDI of laser

wavelengths and leaf moisture content. Expanding this to include wood moisture content can

lead to the use of TLS to estimate both leaf and wood water content, if a relationship between

NDI and wood moisture content can be established. This can lead to estimating the total

vegetation water content (kg m-2) from TLS data. VWC is a different water status metric to

CWC, as the former can be used in retrieving soil moisture content under vegetation from active

and passive microwave remote sensing (Njoku and Entekhabi, 1996; Yilmaz et al., 2008), while

the latter is a vegetation stress indicator and a parameter of interest in studying the water cycle

and its role in the global climate change (Clevers et al., 2010; Mendiguren et al., 2015). CWC

represents the total moisture content in canopy per unit ground surface area, while VWC

represents the total moisture content in leaves, branches and stems per unit ground surface area.

However, it is expected that this method may be able to only detect moisture in the surface and

outer layer of stems and branches, and not to quantify total moisture in wood components. In

vegetation with small stems filled with watery sap, such as the willow plots scanned in this

study, this method may be able to better-quantify the wood moisture content.

EWT vertical profiles can also represent the vertical heterogeneity in LMA (g cm-2), as EWT

was found to be highly correlated to LMA in this study and also in Junttila et al. (2019). The

EWT – LMA relationship can be used to generate 3D LMA point clouds from the 3D EWT

estimates, but the accuracy of the estimation may be of concern as the errors will accumulate.

Although LMA is not a water status metric, it is an important trait in plant growth rate

(Gutschick and Wiegel, 1988; Poorter et al., 2009). Another metric that is correlated to EWT

and LMA is FMC (%), which can be estimated as EWT divided by LMA. Coupling the 3D

EWT estimates retrieved from TLS with LMA measurements retrieved from field spectroscopy,

destructive sampling, or hyperspectral imagery, can lead to estimating FMC in 3D. Studying

FMC distribution in 3D may lead to a better characterization of forest fire ignition and

162

propagation, as FMC in each canopy layer can be quantified. The results obtained in this

research showed that NDI can be used to estimate FMC directly, but also highlighted that the

NDI – FMC relationship was species-specific and highly influenced by the variation in LMA,

and testing this approach in a real forest environment is still needed. Figure 8-4 summarizes the

different metrics that can be retrieved from the 3D EWT point clouds.

Figure 8-4. Retrieving other vegetation water status metrics and LMA from the 3D EWT point

cloud.

Towards improving the accuracy of EWT estimation from optical RS data:

In this study, although the TLS data were collected at the plot level, EWT was studied at leaf

and canopy levels only, and the understory was excluded during the point cloud processing to

reduce computational time. Estimating EWT at the plot level can allow a direct comparison

with EWT estimates retrieved from spaceborne and airborne optical RS data. By generating 3D

EWT estimates at the plot level in forest plots that correspond to specific pixels in the satellite

imagery, TLS can provide ground truth data to validate the satellite estimation of EWT.

Furthermore, the 3D EWT estimates allow the study of the EWT distribution separately in each

component of the plot: in the understory, in the whole canopy excluding the woody materials,

and in each canopy layer, and thus can be used to study how EWT of these different components

contributes to the EWT estimated from optical RS data per pixel. The radiative transfer

modelling conducted in this study showed that the upper canopy was the main component that

163

dominated the forest plot reflectance and the change in NDWI, but a direct comparison between

TLS estimated EWT and satellite estimated EWT is still needed. With this achieved, TLS can

then serve as a tool to calibrate the satellite estimated EWT by excluding the understory and

woody materials’ influence on the estimation, aiming at a more accurate estimation of canopy

EWT from spaceborne and airborne imagery.

Towards improving spaceborne sensors early-detection of forest stress and wildfire risk:

The results obtained from the DART simulations conducted in this study revealed that the

canopy top layers (top four meters) dominated the forest plot reflectance in the two plots

examined. This suggested that spaceborne sensors can monitor the water status of canopy top

layers only, and that the water status metrics estimated from optical RS data may not necessarily

represent the whole canopy. For instance, the results of the simulations suggested that using

Sentinel-2 imagery to estimate EWT in the two forest plots simulated would result in an

overestimation of EWT, because the majority of the received satellite signal came from the

canopy top, which has higher EWT than the canopy bottom according to the EWT vertical

profiles generated from TLS. However, the EWT vertical profiles can be used to expand the

satellite estimation of EWT to include lower canopy layers as well as upper canopy layers.

Figure 8-5 demonstrates the use of TLS to expand the satellite estimation of EWT in the forest

plot scanned in the Wytham dataset. Although EWT vertical profiles cannot be generated in

forest plots corresponding to every pixel in the satellite imagery, generating EWT vertical

profiles in multiple plots in the same forest may be sufficient to represent the EWT vertical

heterogeneity in the forest. However, the plots must represent the different species in the forest.

164

Figure 8-5. The use of EWT vertical profiles generated from TLS to determine EWT reduction

coefficients that can be used to retrieve EWT of lower canopy layers from EWT of canopy top

layers, estimated from optical RS data. Additional layers can then be added to the 2D EWT

distribution map generated from the satellite imagery, with each layer representing EWT in a

lower canopy layer.

During drought conditions, trees were found to lose water content unequally from different

canopy layers, as shown in the EWT vertical profiles generated in the indoors dry-down

experiment and in the Exhibition Park datasets. Although the temporal changes in EWT vertical

profiles still need to be examined in forest canopies, it is expected that during drought

conditions trees may attempt to maintain healthy canopy top layers and will most likely lose

more moisture from canopy bottom layers. This is because in a forest, canopy top leaves are

typically sun leaves (Chazdon and Fetcher, 1984), and trees tend to dedicate more nutrients and

water to sun leaves to optimize photosynthesis (Hirose and Werger, 1987; Hikosaka, 2004). In

the case of forest canopies losing more moisture from lower canopy layers than from upper

canopy layers during a drought, a satellite sensor may not be able to detect the water stress that

started in the canopy bottom. Using TLS to study the drying patterns of forest canopies and

how the trees react to drought conditions and redistribute the resources to different canopy

layers can help addressing this limitation. EWT vertical profiles generated during drought

conditions can quantify how much moisture was lost from the different canopy parts, then used

to expand the satellite estimation of EWT to account for the water stress in lower canopy layers,

if any (Figure 8-5). The threshold used in early-detection of wildfire risk using a satellite sensor

165

can then be adjusted to include the lower EWT in canopy bottom layers, which may improve

the decision making during heatwaves and droughts.

Towards the use of dual-wavelength TLS in vegetation health monitoring

This study showed that dual-wavelength TLS can provide 3D EWT estimates at the canopy

level in mixed-species vegetation sites (forest and urban park tree plots) using general, site-

specific, species-independent EWT estimation models. It was also possible to develop a site-

and species-independent general model that was successfully applied in the forest and urban

park tree plots and achieved EWT estimation accuracy close to that achieved using the site-

specific models. This was mainly because the leaf structure coefficient (N) of the species

involved in this study ranged between 1.5 and 2, according to the PROSPECT simulations, and

the general EWT model was calibrated using leaf samples that covered this range. This

suggested that the general EWT model derived in this study can be used to estimate canopy

EWT at the plot and landscape levels in mixed-species deciduous woodlands. Species

classification and species-specific models will not be needed, as long as the species present in

the site have N values ranging between 1.5 and 2. The leaf samples collected in this study from

11 deciduous species were all found to be in this range of N values, further indicating that the

developed model can be considered a general EWT model for deciduous species. The model

can be further improved by adding more species to the calibration data, and also by adding more

leaf samples in the low EWT region (less than 0.009 g cm-2). However, it was not possible to

use the general EWT model to estimate EWT for holly trees, which had thick leaves with waxy,

glossy surfaces (N > 2 according to PROSPECT simulations). Thus, in mixed-species sites, the

presence of trees from species that have significantly thicker leaves than those used in

calibrating the model (N > 2) can complicate the use of this method at the landscape level. Such

trees will require their own EWT estimation model and must be identified and segmented in the

point cloud. Leaf sampling will also be needed to build the species-specific models.

One solution for this issue is to use the NDI values to auto-detect trees in the point cloud, if

any, that have N > 2, as such trees will have significantly higher NDI values than the other

species, making them appear as statistical outliers. For instance, NDI values of all trees scanned

in this study, both in the forest and urban park tree plots, ranged between 0.17 and 0.42, with

mean NDI of 0.26 and standard deviation of 0.044. Using the mean and standard deviation,

holly trees can be auto-detected in the point cloud by using a threshold of the mean + one

standard deviation (the threshold = 0.3, NDI of holly = 0.42, highest NDI in all other

166

trees = 0.29). Afterwards, the general EWT model can be applied to all trees in the point cloud,

while the species-specific model can be applied to the auto-detected holly trees. To further

automate the process, general EWT estimation models derived from PROSPECT simulations

can be used instead of those derived from destructive sampling. An EWT estimation model

corresponding to N of 1.7, derived from PROSPECT simulations, can be used as a general

model for the species that have N values between 1.5 and 2. Furthermore, it was observed that

forest canopies had thicker leaves than tree canopies in the urban park tree plot, even if they

were from the same species. For instance, beech leaf samples collected from the forest plot

were thicker than those collected from the beech tree in the urban park tree plot (Figure 8-1).

Thus, the use of general EWT models based on habitat type can further improve the accuracy

of the EWT estimation. The PROSPECT simulations suggested that an EWT model

corresponding to N of 1.5 can represent deciduous broadleaf canopies in urban tree plots, and

a general model corresponding to N of 1.8 can be suitable for deciduous broadleaf trees in

woodlands. A general EWT model corresponding to N of 2.3 can be used for species with thick

leaves (N > 2), such as holly, as the highest value of N for non-senescent, dicot leaves is 2.5

(Jacquemoud and Baret, 1990). Table 8-1 shows the suggested general EWT models derived

from the PROSPECT simulations. Figure 8-6 shows the processing steps to generate the 3D

EWT point clouds.

Table 8-1. General EWT estimation models based on the PROSPECT simulations.

Habitat Species N EWT estimation model

Urban tree

plot

Beech

Swedish Whitebeam

Ash

Snake-bark maple

Lime

Grey Alder

Common Alder

Poplar

1.5 EWT (g cm-2) = 0.0690 × NDI – 0.0077

Deciduous

woodland

Sycamore

Oak

Beech

Ash

1.8 EWT (g cm-2) = 0.0733 × NDI – 0.0076

Urban tree

plot Holly 2.3 EWT (g cm-2) = 0.0794 × NDI – 0.0074

General

model Deciduous 1.7 EWT (g cm-2) = 0.0720 × NDI – 0.0076

General

model

Species with thick

leaves (N > 2) 2.3 EWT (g cm-2) = 0.0794 × NDI – 0.0074

167

Figure 8-6. A flowchart of the LiDAR data processing pipeline to generate 3D EWT point

clouds in mixed-species sites using the general EWT estimation models derived from

PROSPECT simulations.

The aforementioned approach and the general EWT estimation models described in Table 8-1

still need to be tested by reprocessing the point clouds collected in this study and comparing

the accuracy achieved using the site-specific models, derived from destructive sampling, and

that achieved using the general EWT models derived from the PROSPECT simulations.

Furthermore, general EWT models still need to be developed for coniferous species, and

transferring the models suggested in Table 8-1 to other types of forests (boreal forests and

tropical rainforests) also needs to be examined.

The use of general EWT models, being independent of species and transferable to different

sites, can make dual-wavelength TLS a useful tool for operational landscape-scale EWT

estimation in mixed-species vegetation areas, with no need for a prior knowledge of the species

present in the site. Time series of the change in EWT can be produced at much higher temporal

resolution than spaceborne sensors. For instance, during drought conditions, the daily change

in EWT can be monitored, with the potential to even detect the changes in EWT throughout the

day. As the EWT estimates are provided in 3D, the change in EWT vertical profiles can be

observed, and trees reaction to drought conditions, regarding how they redistribute water and

resources, can be studied at very high spatial and temporal resolutions. However, one challenge

associated with the use of TLS to estimate EWT at the landscape level is the scan time required

to cover large areas of vegetation. For instance, applying this method to generate 3D EWT

estimates in a one-hectare forest plot, using 20 m × 20 m scan grid and 3 mm point spacing,

would require at least 25 hours of scanning (5 days of scanning, 5 hours per day). This would

reduce the temporal resolution of the time series of the change in EWT produced for such large

Collect and process

LiDAR data to generate NDI point clouds

Tree segmentation

and leaf/wood separation

Auto detection of trees with thicker leaves

using their NDI values

Apply general EWT estimation model based on

habitat type

Apply suitable EWT model to

trees with thicker leaves, if any

Generate 3D EWT point

cloud of the site

168

areas, which may also be further reduced because of the wind effects, as scans cannot be

conducted in windy conditions. The scanning time can be reduced by increasing the point

spacing (reducing the scan resolution) or increasing the scan grid size. However, at the

landscape level, TLS can only be suitable for detecting the weekly or monthly changes in EWT,

depending on the size of the area to be scanned. Scanning only a set of plots that represent the

different species in the site can be more useful, as these plots can be scanned daily and the rapid

changes in EWT can be monitored. Another factor that needs to be taken in consideration while

applying this approach at the plot and landscape levels is the effect of the variation of the canopy

LAI in heterogeneous sites. Although the use of a vegetation index can be sufficient to reduce

the effects of incidence angle, leaf internal structure, and leaf BRDF, canopy LAI can influence

the accuracy of the EWT estimation using vegetation indices. Thus, at the plot and landscape

levels, LAI measurements are needed, and canopy EWT should be quantified as the product of

EWT and LAI.

Towards the use of dual-wavelength airborne LiDAR in vegetation health monitoring

Recently, the Optech Titan (Teledyne Optech) multispectral airborne LiDAR sensor was

launched, being the first multispectral LiDAR system commercially available. The system

collects data in three channels, employing 1550 nm SWIR, 1064 nm NIR, and 532 nm visible

wavelengths. Channels 1 and 2 have similar beam divergence (0.35 mrad), and also have similar

wavelengths to those utilized in the DWEL and SALCA dual-wavelength TLS systems, which

have been previously linked to EWT. Thus, the system has the potential to provide 3D EWT

estimates at the landscape level, if the point clouds from the two different channels could be

accurately aligned. Similar to TLS, the use of airborne LiDAR in EWT estimation at the

landscape level in mixed-species woodlands can be complicated by the presence of species with

significantly thicker leaves than the other species in the site, preventing the use of general EWT

estimation model derived from destructive sampling. Unlike TLS, airborne LiDAR data

collection does not require accessing the forest, making it more feasible for woodlands with

limited or no accessibility. However, this also limits the amount of information obtainable about

the species present in the woodland, making identifying the trees with thicker leaves more

challenging. In this case, the EWT estimation models presented in Table 8-1, and the processing

steps shown in Figure 8-6, can be used, without the need for accessing the woodland to collect

leaf samples to build EWT estimation models, or to identify species with significantly thicker

leaves. However, further investigation is still needed for senescent leaves, as it was found in

this study that the level of senescence affected the EWT estimation model and prevented the

169

develop of site- and species-independent EWT estimation model. It was found that a general

EWT model with N of 2.5 can represent the senescent trees in the urban park tree plot. However,

the model did not represent the NDI – EWT relationship for the willow plots, and site-specific

models were mandatory in this case.

Overall, this study showed the potential of using commercially-available TLS instruments to

provide important insights into the EWT distribution within canopy, by mapping the EWT at

canopy level in 3D. The proposed approach can serve as a powerful tool to study the variation

of EWT within the canopy and between different species, can provide high spatial and temporal

EWT estimations, is independent of the cloud coverage and solar illumination, and has the

potential to estimate EWT predawn. This approach can also be used in detecting temporal

changes in EWT, showing how the vertical profiles of EWT change during drought conditions,

and how trees redistribute water and resources to cope with the water deficiency. Being the first

study to successfully map canopy EWT in 3D in forest and urban park tree plots, and to provide

detailed vertical profiles of canopy EWT, the method proposed in this study can help fill the

gap in the literature regarding quantifying and studying the vertical heterogeneity in canopy

biochemistry. This study showed that canopy EWT varied vertically. It was always higher in

the canopy top (sun leaves) than in the canopy bottom (shade leaves) in forest canopies, while

this was not the case in the urban part tree plot, where different species and different trees within

each species had different EWT vertical profiles. It was found that the EWT vertical profiles

seemed to be affected by the illumination conditions and the location of sun and shade leaves

layers in the canopy. EWT and LMA were found to be highly correlated, which affected the

EWT distribution within the canopy, as leaves with higher LMA, typically thicker, held more

moisture than thinner leaves. Other key findings were that trees did not lose moisture equally

from all the canopy layers while being water-stressed, and that they seemed to attempt to

maintain EWT in sun leaf layers unchanged, while losing more moisture from shade leaf layers.

This study also introduced a method to integrate the vertical heterogeneity in canopy

biochemistry into 3D radiative transfer modelling by utilizing the EWT vertical profiles

retrieved from TLS into the DART 3D RTM. The simulations showed that the upper canopy

layers dominated the forest plot reflectance and that the spaceborne sensor was not able to detect

severe water stress in the bottom canopy layers. This suggested that spaceborne optical sensors

can monitor the change in EWT in the canopy top layers only, which should be taken into

consideration while monitoring forest health during drought conditions. This can improve the

decision making regarding preventing and fighting forest wildfires.

170

The method proposed in this study can be improved by coupling TLS data with in situ

multispectral or hyperspectral data in order to quantify the LMA distribution within the canopy

and establish relationships between the EWT and LMA distribution. Furthermore, chlorophyll

content can be estimated using the multispectral or hyperspectral data, and the relationship

between EWT and chlorophyll content vertical profiles can be studied, especially during

drought conditions. The tree 3D models generated in this study, and used in the radiative

transfer modelling, can be improved by coupling the TLS data with airborne LiDAR data to

reduce the effects of occlusion. The tree 3D models were used to estimate canopy LAI, and in

situ measurements of LAI using hemispherical imagery or litter traps are needed to evaluate

this LAI estimation method. Also, the radiative transfer modelling conducted in this study,

despite the results being close to those obtained from actual satellite data, can be improved by

collecting leaf spectra of the understory vegetation in the forest plots and model the understory

as turbid medium. The results are also expected to improve, especially in the NIR reflectance

region, if TLS and airborne LiDAR data are used to reconstruct the forest plot, as the canopy

LAI will be more realistic. Finally, this study utilized single-wavelength, commercial TLS

instruments as no commercial multispectral TLS systems have yet become available. Although

it showed that combining data from two commercially-available TLS systems can be an

alternative to commercial multispectral TLS systems, the TLS instruments used in this study

were pulsed systems. Commercial, full-waveform multispectral TLS systems, if developed,

have the potential to provide more information about the canopy structure and biochemistry,

especially inside the canopy. Next steps include combining the EWT vertical profiles with the

canopy LAI in order to quantify the water content (g) in each canopy layer and characterise the

whole-tree leaf water status and total water content, and applying the method predawn to

examine its potential in measuring the predawn canopy EWT.

8.4 Conclusions

This research showed the potential of commercial dual-wavelength TLS as a powerful tool to

monitor vegetation water status by estimating canopy EWT in three dimensions. Key findings

of this study were: (1) EWT exhibited vertical heterogeneity within canopy, which varied

between species, sites, and individual trees, (2) trees were found to lose moisture content

unequally from different canopy layers during drought conditions, (3) forest plot reflectance

was dominated by canopy top layers, and severe water stress that started in lower canopy was

not detected by the satellite sensor until it affected the top four metres of canopy. The presented

method can help improve understanding of 3D biochemistry and resource allocation in trees,

171

and provide new insights into how trees react to heatwaves and droughts. The method can also

provide EWT vertical profiles and allow the study of their temporal changes, and such

measurements cannot be obtained using optical RS spaceborne and airborne sensors. EWT

estimates retrieved from TLS can fill the gaps in the time series of EWT temporal changes

produced from optical RS data. The proposed method also has the potential to be used to

calibrate and validate the optical RS estimates of EWT, and to provide EWT estimates predawn.

172

173

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